Flatten the Strategy inheritance

Nobody has claimed this yet.

## Proposed refactor

Flatten the Strategy inheritance:
Part of #10416

### Motivation

Reduce coupling between strategies, reduce unintentional overrides/inheritance and avoid silent failures
eg:
1. DeepSpeed inherited configure_ddp() from DDP, which is unnecessary and error-prone.
2. DDP and FSDP have totally different distributed behavior, but with current inheritance, adding a DDP strategy method will automatically enable this method for FSDP

### Pitch
Tasks list:
- FSDP inherit ParallelStrategy instead of DDP
- DeepSpeed inherit ParallelStrategy instead of DDP
- [RFC] DDPShard inherit ParallelStrategy instead of DDP
- [RFC] DDPShardSpawn inherit ParallelStrategy instead of DDPSpawn
- [RFC] TPUSpawn inherit ParallelStrategy instead of DDPSpawn

### Additional context

The downside of this task will be code duplications.
Align DDP/DDPSpawn precess group creation #11643 and Collective refactor #9414 will reduce the duplication.
______________________________________________________________________

#### If you enjoy Lightning, check out our other projects! ⚡

- [**Metrics**](https://github.com/PyTorchLightning/metrics): Machine learning metrics for distributed, scalable PyTorch applications.

- [**Lite**](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html): enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

- [**Flash**](https://github.com/PyTorchLightning/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

- [**Bolts**](https://github.com/PyTorchLightning/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

- [**Lightning Transformers**](https://github.com/PyTorchLightning/lightning-transformers): Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.

cc @justusschock @awaelchli @akihironitta @rohitgr7