FSDP with HYBRID_SHARD loss doesn't improve with more nodes

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### Bug description

When using the FSDP strategy with HYBRID SHARD set, the loss behaves as if only one node is training. When it is set to FULL_SHARD/etc the loss drops as expected when more nodes are added and batch size is left constant. I have verified NCCL connections are working correctly as everything behaves as expected when using FULL_SHARD.

### What version are you seeing the problem on?

v2.4

### How to reproduce the bug

_No response_

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```

### More info

_No response_

Start by reproducing the reported behavior with FSDP HYBRID_SHARD and a fixed batch size while varying the node count, then compare it with FULL_SHARD. Check the FSDP strategy and distributed-training tests or entry points that cover shard strategies; done means loss improves with additional nodes under HYBRID_SHARD as expected.

Written by the indexing model from the issue text.