Gradient checkpointing and ddp do not work together

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### Bug description

Am launching a script taht trains a model which works well when trained without ddp and using gradient checkpointing, or using ddp but no gradient checkpointing, using fabric too. However, when setting both ddp and gradient checkpointing, activate thorugh gradient_checkpointing_enable() function of huggingface, we get error
```
[rank0]: File "/home/.../v2/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
[rank0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[rank0]: RuntimeError: expect_autograd_hooks_ INTERNAL ASSERT FAILED at "../torch/csrc/distributed/c10d/reducer.cpp":1591, please report a bug to PyTorch.

```

Scripts where launched with
```
fabric = Fabric(accelerator="gpu",
loggers=loggers,
precision=opt.precision,
strategy=DDPStrategy(process_group_backend="nccl", find_unused_parameters=False, static_graph=True)
)
```

When i launch with options `strategy=DDPStrategy(process_group_backend="nccl", find_unused_parameters=True, static_graph=False)`, I get error instead:

```
[rank0]: Parameter at index 560 with name reader.decoder.transformer.h.11.mlp.c_proj.bias has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration.
```

Thanks in advance for your help.

### 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 reducing the reported Fabric setup to a reproducible script combining DDPStrategy with Hugging Face gradient_checkpointing_enable(), and collect the missing version and environment details. Compare the static_graph/find_unused_parameters configurations and inspect the DDP interaction with gradient checkpointing; done means the combination no longer raises either reported reducer error or the limitation is clearly documented.

Written by the indexing model from the issue text.