AsyncCheckpointIO Should Clone() to CPU not on GPU

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### Bug description

In [pytorch-lightning/src/lightning/pytorch/plugins/io/async_plugin.py](https://github.com/Lightning-AI/pytorch-lightning/blame/master/src/lightning/pytorch/plugins/io/async_plugin.py#L100)

Tensor in Model Checkpoint are cloned() to prevent race condition when doing async uploading in threads.

```
# snapshot the checkpoint payload on the caller thread to avoid races with parameter mutation
def _clone_tensor(t: torch.Tensor) -> torch.Tensor:
"""Clones a tensor on the caller thread."""
# detach to avoid autograd history and clone to take a point-in-time copy
return t.detach().clone()
```

However, here it is clone() from GPU memory to GPU memory. Given GPU memory is often limited, this step is dangerous when num_thread go up.

A more clean solution is to clone the tensors to CPU. This achieves the same purpose without using without using GPU memory. CPU memory is abundant most of the time.

```
# snapshot the checkpoint payload on the caller thread to avoid races with parameter mutation
def _clone_tensor(t: torch.Tensor) -> torch.Tensor:
"""Clones a tensor on the caller thread."""
# detach to avoid autograd history and clone to take a point-in-time copy
return t.detach().cpu().clone()
```

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

master

### Reproduced in studio

_No response_

### How to reproduce the bug

```python

```

### Error messages and logs

If cloning on GPU memory, this is dangerous for large model checkpoint (e.g. 15GB in our case)

Cloning to CPU
```
[ASYNC CHECKPOINT BEFORE clone] GPU 0: allocated=21.54 GB, reserved=124.30 GB
[ASYNC CHECKPOINT AFTER clone] GPU 0: allocated=21.54 GB, reserved=124.30 GB
```

Cloning on GPU
```
[ASYNC CHECKPOINT BEFORE clone] GPU 0: allocated=21.54 GB, reserved=124.30 GB
[ASYNC CHECKPOINT AFTER clone] GPU 0: allocated=37.54 GB, reserved=124.30 GB
```

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.6.0):
#- PyTorch Version (e.g., 2.5):
#- 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_

cc @ethanwharris

Start in src/lightning/pytorch/plugins/io/async_plugin.py at _clone_tensor, where checkpoint tensors are detached and cloned. Verify the snapshot moves tensor data to CPU without retaining an extra GPU copy, while preserving the race-avoidance behavior needed by asynchronous uploads. Done means the async checkpoint path no longer duplicates checkpoint tensors in GPU memory.

Written by the indexing model from the issue text.