Deprecate prepare_seq2seq_batch
# What does this PR do?

This PR officially deprecates `prepare_seq2seq_batch` to prepare its removal in Transformers v5. As discussed before, the proper way to prepare data for sequence-to-sequence tasks is to:
- call the tokenizer on the inputs
- call the tokenizers on the targets inside the context manager `as_target_tokenizer`

When only dealing with input texts without targets, just using the tokenizer call works perfectly well.

For `mBART` and `mBART50` tokenizers the source and target language can be specified at init or changed at any time by setting the attributes `.src_lang` and `.tgt_lang`.

Here is a full example showing how to port old code using `prepare_seq2seq_batch` to the new way in the case of an mBART tokenizer (remove the mentiones of `src_lang` and `tgt_lang` for other tokenizers:
```
tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro')
batch = tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts, padding=True, truncation=True, src_lang="en_XX", tgt_lang="ro_RO", return_tensors="pt")
``` 
becomes
```
tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro', src_lang="en_XX", tgt_lang="ro_RO")
batch = tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt")
with tokenizer.as_target_tokenizer():
    targets = tokenizer(tgt_texts, padding=True, truncation=True, return_tensors="pt")
batch["labels"] = targets["input_ids"]
```
The languages can be changed at any time with
```
tokenizer.src_lang = new_src_code
tokenizer.tgt_lang = new_tgt_code
```

This PR fixes a few things in `MBartTokenizer` and `MBartTokenizerFast` for the new API to work completely and removes all mentions of `prepare_seq2seq_batch` from the documentation and tests (except the test of that method in the common tests). It was already not used anymore in the seq2seq example `run_seq2seq`.
Hi all! Sorry, but this seems to be cleaner:  (Some feature request: #14255)
```python
encoded_train_dataset = train_dataset.map(
    lambda batch: tokenizer.prepare_seq2seq_batch(
        batch['text'], batch['summary'], padding='max_length', truncation=True, max_length=256, max_target_length=64
    ),
    batched=True,
    remove_columns=train_dataset.column_names,
)
```