Generally, yes, data points in the FID are acquired uniformly. So, the sampling interval will simply be the acquisition time divided by the number of points.
As Buck Thorn pointed out in some (now deleted) comments, however, there is a slight subtlety in that FIDs in NMR are inherently complex-valued, whereas computers only store real numbers (i.e. complex numbers must be stored as a pair of real numbers). I'm not sure what Varian machines do, but on Bruker machines, the data array actually alternates between real and imaginary points. So, data point 1 is actually the real part of the first complex data point, data point 2 is the imaginary component, etc. So instead of $N$ real data points you actually get $N/2$ complex data points, with an effective sampling interval that is twice of what was earlier mentioned.
This is not as big a problem as it might seem. Using $N/2$ complex data points sampled at half the rate for a complex FFT, or $N$ real data points for a real FFT, will actually give you the same spectral window by the Nyquist theorem. The resulting spectrum will also be the same, although you may need to use the
fftshift() function to correct the frequencies.
There is more in-depth discussion of this issue in this article: Turner & Hill, J. Magn. Reson. 1986, 66, 410.
I'm aware of examples where 1D data is acquired in a non-uniform manner and the missing points are reconstructed. See e.g. Ndukwe et al., ChemPhysChem 2016, 17, 2799. These are pretty niche, though, and I'd say that virtually all 1D data you will encounter is uniformly sampled.
2D data is a lot more complicated. Generally, these are functions of two time variables: $S = S(t_1, t_2)$, so they are stored as matrices, built up of many FIDs.
Within each FID, $t_1$ is a constant, and $t_2$ varies. Between each FID, the value of $t_1$ changes. So, it's often the case that each row of the matrix is one FID, and so the columns represent different values of $t_2$, and the rows represent different values of $t_1$. However, you should consult the manufacturer's manuals as to the exact format in which this data is stored (row-major or column-major).
The $t_2$ dimension is entirely analogous to 1D spectra (essentially, 2D NMR is about collecting many 1D spectra with different values of $t_1$). Hence, it's still almost always the case that data is uniformly sampled in the $t_2$ dimension.
However, the $t_1$ dimension is way more complicated, for three reasons:
There is a similar issue to the real/complex difference mentioned previously for 1D data. In the case of 2D data, it's very often the case that the first and second FIDs actually have the same value of $t_1$, but are actually different experiments designed to yield the real and imaginary part of a complex number (the States method). Alternatively, the first and second FIDs have the same value of $t_1$, and are meant to allow you to regain the real and imaginary parts of a complex number through linear combination (echo-antiecho method). Alternatively, the first and second FIDs actually have different values of $t_1$ (TPPI method, or magnitude-mode spectra).
In general, it is not sufficient to directly perform Fourier transformation on a 2D data matrix read from a file without any extra processing.
The entire reason for this is so that positive and negative frequencies can be discriminated in the $t_1$ dimension after Fourier transformation, and is technically known as quadrature detection. This is an extremely important topic in 2D NMR and there is no way I can explain this fully; I instead refer you to a textbook such as Keeler's Understanding NMR Spectroscopy.
The 'complex' signals in $t_2$ are in fact orthogonal to the 'complex' signals in $t_1$, in the sense that the imaginary units used in both dimensions are to be considered separately. That means that, instead of a 2D matrix of complex numbers $a + bi$, you actually have a 2D matrix of hypercomplex numbers (or quaternions) $a + bi + cj + dij$, where $i$ and $j$ represent the imaginary units in the two dimensions. The number $a$ is real in both dimensions, $b$ and $c$ are imaginary in one of the two dimensions each, and $d$ is imaginary in both dimensions. On Bruker machines, each of these four components are stored in separate files. I don't know what Varian data looks like, but I expect it to be broadly similar.
In recent years non-uniform sampling (NUS) in the $t_1$ dimension is becoming more and more common, and there is a non-negligible chance that any random 2D dataset you obtain from someone in fact uses non-uniform sampling. In fact, when people talk about non-uniform sampling in NMR they are almost always referring to the $t_1$ dimension of 2D NMR (or higher-dimensional NMR). A simple Google search for NUS in NMR will likely bring up several reviews.