Merging pattern for FFT solutions
You can compute the DFT of the total signal from the DFTs of its parts but this is not efficient. What you would need to do is interpolate each DFT to the length of the total array (which is equivalent to zero-padding the sub-blocks to length $N$ before computing the individual FFTs), and then combine the interpolated DFTs with appropriate complex factors. This is computationally less efficient than going back to the time domain and computing the FFT of the whole sequence.

There is no recursion involved (unlike your examples of recursive mean and variance computation), simply because the data blocks are not related to each other. If you were to have a sliding window with a fixed length and you wanted to recompute the DFT after shifting the window by one sample, then you would indeed get a recursive formula for the DFT. This is known as the sliding DFT.