ModelCheckpoint Callback state loader with missing dir

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### Bug description

Loading a checkpoint with the ModelCheckpoint callback on a different machine (or with a missing/moved "best_model_path" dir) results in an error and crash.

A common use case for me is to train a model (with .ckpt stored elsewhere i.e Neptune), and then pull the checkpoint from that model to another machine to continue training later. This used to work in older versions, but now breaks. Currently, the code deals with situations where the directory structure has changed, but not for larger changes in the absolute file-structure.

### How to reproduce the bug

```python
1. Run a model with ModelCheckpoint
2. Save the whole model as a .ckpt
3. Resume the .ckpt run on another machine.
```

### Error messages and logs

# Error messages and logs here please

```
Restoring states from the checkpoint path at /home/kyle/v6x_wsl/vrew/TTS/training/nv_fast_pitch/cache/TTS-781/step=100000.ckpt
/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:345: UserWarning: The dirpath has changed from '/home/kyle/v6x/vrew/TTS/training/nv_fast_pitch/TTS-781/.neptune/Untitled/TTS-781/checkpoints' to '/home/kyle/v6x_wsl/vrew/TTS/training/nv_fast_pitch/TTS-798/.neptune/Untitled/TTS-798/checkpoints', therefore `best_model_score`, `kth_best_model_path`, `kth_value`, `last_model_path` and `best_k_models` won't be reloaded. Only `best_model_path` will be reloaded.

Error executing job with overrides: []
Traceback (most recent call last):
File "/home/kyle/v6x_wsl/vrew/TTS/train.py", line 25, in train
trainer.fit(model, datamodule=datamodule, ckpt_path=checkpoint)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit
self._call_and_handle_interrupt(
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 648, in _call_and_handle_interrupt
return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 735, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1166, in _run
results = self._run_stage()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1252, in _run_stage
return self._run_train()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1283, in _run_train
self.fit_loop.run()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 200, in run
self.advance(*args, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 271, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 201, in run
self.on_advance_end()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 241, in on_advance_end
self._run_validation()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 299, in _run_validation
self.val_loop.run()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 207, in run
output = self.on_run_end()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 201, in on_run_end
self._on_evaluation_end()
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 265, in _on_evaluation_end
self.trainer._call_callback_hooks(hook_name, *args, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1597, in _call_callback_hooks
fn(self, self.lightning_module, *args, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 320, in on_validation_end
self._save_last_checkpoint(trainer, monitor_candidates)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 653, in _save_last_checkpoint
self._save_checkpoint(trainer, filepath)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 394, in _save_checkpoint
logger.after_save_checkpoint(proxy(self))
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/utilities/rank_zero.py", line 32, in wrapped_fn
return fn(*args, **kwargs)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loggers/neptune.py", line 572, in after_save_checkpoint
model_name = self._get_full_model_name(checkpoint_callback.best_model_path, checkpoint_callback)
File "/home/kyle/anaconda3/envs/tts/lib/python3.8/site-packages/pytorch_lightning/loggers/neptune.py", line 596, in _get_full_model_name
raise ValueError(f"{model_path} was expected to start with {expected_model_path}.")
ValueError: /home/kyle/v6x/vrew/TTS/training/nv_fast_pitch/TTS-781/.neptune/Untitled/TTS-781/checkpoints/step=100000.ckpt was expected to start with /home/kyle/v6x_wsl/vrew/TTS/training/nv_fast_pitch/TTS-798/.neptune/Untitled/TTS-798/checkpoints/.
```

### Environment

```

#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0): 1.7.7
#- Lightning App Version (e.g., 0.5.2): N/A
#- PyTorch Version (e.g., 1.10): 1.12.1
#- Python version (e.g., 3.9): 3.8
#- OS (e.g., Linux): Linux(WSL)
#- CUDA/cuDNN version: 11.3/8.3.2
#- GPU models and configuration: 3090x2
#- How you installed Lightning(`conda`, `pip`, source): conda
#- Running environment of LightningApp (e.g. local, cloud): local

```

### More info

It seems like the simplest and maybe most straight forward solution is to not restore the ModelCheckpoint state at all if the directory has changed. There are more complex solutions (like checking each field) but given that this specific checkpoint is tightly coupled with the file structure it seems ill advised.

Start in pytorch_lightning/callbacks/model_checkpoint.py at state restoration, on_validation_end, and _save_last_checkpoint, then follow the failure into pytorch_lightning/loggers/neptune.py at after_save_checkpoint and _get_full_model_name. Reproduce resuming a checkpoint whose best_model_path belongs to a different machine or directory. Done means the run no longer crashes when the checkpoint directory has changed and the callback state behavior is covered.

Written by the indexing model from the issue text.