Calling `trainer.fit` twice with spawn strategies won't work as expected

Nobody has claimed this yet.

### Bug description

Since data in the spawned region is not shared with the main process, the spawn launcher saves a checkpoint of the weights before finishing that is then loaded on the main process:

https://github.com/Lightning-AI/lightning/blob/984f49f7195ddc67e961c7c498ee6e19fc0cecb5/src/lightning/pytorch/strategies/launchers/multiprocessing.py#L190-L195 https://github.com/Lightning-AI/lightning/blob/984f49f7195ddc67e961c7c498ee6e19fc0cecb5/src/lightning/pytorch/strategies/launchers/multiprocessing.py#L162-L168

This means that the optimizer states are not loaded, as well as any other state in the trainer.

This isn't a problem with calling `test/validate/predict` after `fit`.

### Solution

Since this is a silent correctness issue. We should raise an error in the short term.

The launcher can check if `fit` was called and is getting called again, and then raise a `NotImplementedError`.

In the longer term, we can save a full checkpoint that contains all the relevant data and then lift this restriction.

cc @tchaton @justusschock @awaelchli @carmocca @JackCaoG @Liyang90 @gkroiz

Start in src/lightning/pytorch/strategies/launchers/multiprocessing.py, especially lines 162-168 and 190-195, and trace how the launcher saves and restores state around fit. Reproduce two consecutive trainer.fit calls with a spawn strategy, then verify that the second call raises NotImplementedError while test, validate, and predict after fit remain unaffected.

Written by the indexing model from the issue text.