Add metric_key_prefix from training_args
### Feature request

Today, if we create a `Trainer` as in
```
trainer = Trainer(
            model=self.model,  # the instantiated 🤗 Transformers model to be trained
            args=training_args,  # training arguments, defined above
            train_dataset=data_cls["train"],
            eval_dataset=data_cls["develop"],
            compute_metrics=partial(
                compute_metrics,
                fbeta_beta=self.config.early_stopping.fbeta_beta,
            ),
            data_collator=collate_chunks,  # type: ignore
            callbacks=callbacks,  # type: ignore
)
```

and do `trainer.train()`:
1. There's no way to change the prefixes for the `train` dataset metrics (at least that I'm aware of)
2. One can change the prefix for the evaluation dataset from the default `eval/` into anything by changing the above into
```
prefix = "other"
Trainer(
         ....
         eval_dataset={prefix: data_cls["develop"]},
         ...
)
```
However, doing this creates `eval/other_accuracy` due to the way [rewrite_logs](https://github.com/huggingface/transformers/blob/e2e393c6f25205739b5dc9fddd460d7bfab85150/src/transformers/integrations.py#L540) works. Ideally I'd like it to be `other/accuracy`.

My request is to have a clear way in the training_args to add any prefixes to the metrics, either for train or eval datasets.

### Motivation

I want to train multiple models within the same wandb run. As things stand right now, the metrics clash

### Your contribution

I've contributed to other OS projects but I'm not very familiar with the codebase to do the contribution directly. If someone guides me around with a high-level description, happy to do it myself
cc @sgugger <|||||>This seems like a very niche feature which can be achieved by customizing your callback (you can use your own instead of the default ones).<|||||>@sgugger could you elaborate a bit more about what callback(s)? I guess I have to remove one and add mine?

(oh sorry, I just found https://huggingface.co/docs/transformers/main_classes/callback)