Move strategy-specific dataloader logic to the stategies

Nobody has claimed this yet.

## Proposed refactor

### Motivation

Strategies today interact with dataloading, especially in distributed training. It makes sense for the strategy to directly handle this logic.

This would reduce and simplify interactions elsewhere in the trainer, in particular strategy state -> trainer properties -> data connector logic

And would remove the hacky patching that IPUs do right now 😧 https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/strategies/ipu.py#L125-L130

### Pitch

The Strategy interface already offers a `process_dataloader` method:
https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/strategies/strategy.py#L369-L375

However, there's a ton of strategy-specific logic written in the trainer's data connector:

For example, warnings with DDP spawn:
https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/connectors/data_connector.py#L278-L312

generic multi-processing warning:
https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/connectors/data_connector.py#L314-L322

the is_distributed flag is primarily used here within the trainer: https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/connectors/data_connector.py#L324-L330

Distributed sampler kwargs is strategy specific:
https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/trainer.py#L2126-L2129

IPU check: https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/connectors/data_connector.py#L364

In my opinion we could simplify this by moving relevant logic into `strategy.process_dataloader` instead. Common logic can still be abstracted out into utility functions to share across different strategy classes.

We could either:
- augment `process_dataloader` to acept more metadata, such as the trainer's running stage and if this is a dataloader being used for training/validation/test/predict

```py
def process_dataloader(self, dataloader, use: str) -> Union[dataloader, iterable]:
```

or offer multiple APIs that map to the DataLoader hooks: https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/core/hooks.py#L406
```py
def process_train_dataloader(self, dataloader):
def process_val_dataloader(self, dataloader):
def process_test_dataloader(self, dataloader):
def process_predict_dataloader(self, dataloader):
```

Over time, the Trainer flag `replace_sampler_ddp` makes much more sense on the specific distributed strategy constructors instead of on the trainer.

### Additional context

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cc @justusschock @awaelchli @akihironitta @rohitgr7 @ninginthecloud @tchaton @borda

Start with strategies/strategy.py and trainer/connectors/data_connector.py, then inspect the referenced trainer.py and core/hooks.py locations. Compare the existing process_dataloader interface with the DDP spawn, multiprocessing, distributed sampler, and IPU-specific logic. Done means the strategy owns the relevant dataloader behavior and the trainer/data connector no longer coordinates strategy-specific cases.

Written by the indexing model from the issue text.