Refactor distributed_sampler update in `data_connector.py` for distributed strategy

Nobody has claimed this yet.

## Proposed refactor

This issue follows @ananthsub 's https://github.com/PyTorchLightning/pytorch-lightning/issues/11756 to move strategy-specific dataloader logic to the stategies.

### Motivation

`_prepare_dataloader()` function in `data_connector.py` handles the logic whether the dataloader needs to update the distributed sampler when user is using distributed strategy (see the following code link)
https://github.com/PyTorchLightning/pytorch-lightning/blob/cc43d07db1ab77385feff04c01f040c5cad805a9/pytorch_lightning/trainer/connectors/data_connector.py#L360-L372

However, modifying sampler in dataloader can be pushed directly to strategy class. Similar to issue #12216 , we can move the logic to `Strategy` base class. For any strategy classes have `is_distributed=True` flag, they can verify if the dataloader need to update the sampler.

### Pitch

We can move the logic to `Strategy` base class. For any strategy classes have `is_distributed=True` flag, they can verify if the dataloader need to update the sampler.

### Additional context
cc: @edward-io @four4fish @ananthsub

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cc @justusschock @awaelchli @rohitgr7 @ninginthecloud @otaj @tchaton @akihironitta @carmocca

Start with _prepare_dataloader() in pytorch_lightning/trainer/connectors/data_connector.py, especially the linked sampler-update section, then read the Strategy base class and issue #12216 for the related direction. The work is done when the distributed-sampler decision is handled by the appropriate strategy classes while preserving the existing dataloader behavior.

Written by the indexing model from the issue text.