Add `best_model_metrics` to `ModelCheckpoint` callback

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### Description & Motivation

There doesn't seem to be a straightforward way to get the training and validation metrics of the best model as monitored by ModelCheckpoint. Adding `best_model_metrics` attribute to `ModelCheckpoint` can be done easily in a way similar to how `best_model_score` works now.

### Pitch

Accessing training and validation metrics of the best model checkpoint by accessing `trainer.checkpoint_callback.best_model_metrics` right after a `trainer.fit` call is useful for hyperparameter optimization if you want to optimize based on a validation metric or use other custom callbacks to summarize a batch experiment. I am willing to implement this feature.

### Alternatives

1. Running `trainer.test` on the training and validation dataset again after a `trainer.fit` call.
2. Getting records saved by loggers by checking the epoch number in the filename of a checkpoint file.

These two options create unnecessary overheads for a simple feature like this.

### Additional context

https://github.com/Lightning-AI/lightning/discussions/18398

cc @lantiga @borda

Start at the ModelCheckpoint callback and inspect how best_model_score is maintained. Use trainer.fit as the entry point and verify that trainer.checkpoint_callback.best_model_metrics exposes the training and validation metrics associated with the best checkpoint.

Written by the indexing model from the issue text.