Edge case causes incorrect filesystem to be selected for finding cloud checkpoints

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### Bug description

When both of the following happen together:
1. a logger is used with a cloud (e.g `s3://` or `gcs://` protocol) save dir
2. a `ModelCheckpoint` is used without passing a `dirpath`

The desired behaviors are:
1. the checkpoint directory is resolved (via `ModelCheckpoint.__resolve_ckpt_dir`) to `$logger.save_dir/$logger.name/$logger.version/checkpoints` and the `ModelCheckpoint` callback saves them there.
2. `ModelCheckpoint._find_last_checkpoints` will find `$logger.save_dir/$logger.name/$logger.version/checkpoints/last.ckpt`. If will first check if that path exists on the filesystem instantiated in `ModelCheckpoint.__init_ckpt_dir`.

Desired behavior 1 works, 2 does not. There are two bugs:
1. `ModelCheckpoint.__init_ckpt_dir` will [select the wrong filesystem](https://github.com/Lightning-AI/lightning/blob/6eae2310d6dae086596e5bdddd08e8cd3884336e/src/lightning/pytorch/callbacks/model_checkpoint.py#L443) when `dirpath` is `None`, causing `ModelCheckpoint._find_last_checkpoints` to [not find](https://github.com/Lightning-AI/lightning/blob/6eae2310d6dae086596e5bdddd08e8cd3884336e/src/lightning/pytorch/callbacks/model_checkpoint.py#L612) the cloud filepaths.
2. Even if the correct `ModelCheckpoint._fs` were used, `_find_last_checkpoints` returns a set of paths with their protocols stripped (due to the [call to _fs.ls](https://github.com/Lightning-AI/lightning/blob/6eae2310d6dae086596e5bdddd08e8cd3884336e/src/lightning/pytorch/callbacks/model_checkpoint.py#L615)). This causes `_CheckpointConnector_parse_ckpt_path` to then also [select the wrong filesystem](https://github.com/Lightning-AI/lightning/blob/master/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py#L185-L186), resulting in no checkpoints found.
### What version are you seeing the problem on?

v2.0; but likely also present on others

### How to reproduce the bug

1. Use a logger with a cloud save dir
2. Create some cloud checkpoint, e.g. `s3://.../logger_name/logger_version/checkpoints/last.ckpt`
6. From a new job, try to resume training using `ckpt_path="last"`
7. A warning will be emitted about how lightning couldn't find the checkpoint

### Error messages and logs

```
UserWarning: .fit(ckpt_path="last") is set, but there is no last checkpoint available. No checkpoint will be loaded.
```

### Environment

Current environment

```
#- Lightning Component: ModelCheckpoint
#- PyTorch Lightning Version: 2.0.3
#- Lightning App Version: N/A
#- PyTorch Version: 2.0.1
#- Python version: 3.10.11
#- OS: Linux
#- CUDA/cuDNN version: 11.7
#- GPU models and configuration: 1x T4
#- How you installed Lightning: `pip`
#- Running environment of LightningApp (e.g. local, cloud): AWS Sagemaker
```

### More info

Here is my current workaround for S3 checkpoints:

```python
from s3fs import S3FileSystem

class S3ModelCheckpoint(ModelCheckpoint):
def __init__(self, *args: str | None, **kwargs: str | None) -> None:
super().__init__(*args, **kwargs)
self._fs = S3FileSystem()

def _find_last_checkpoints(self, trainer: "L.Trainer") -> set[str]:
ckpts = {"s3://" + ckpt for ckpt in super()._find_last_checkpoints(trainer)}
return ckpts
```

The [Universal Pathlib](https://github.com/fsspec/universal_pathlib) project fixes the behavior of cloud paths so that the procotols aren't stripped off. Could be worth looking into, to prevent these sorts of edge cases from occurring.

cc @awaelchli

Start in src/lightning/pytorch/callbacks/model_checkpoint.py, reading __init_ckpt_dir and _find_last_checkpoints, then inspect src/lightning/pytorch/trainer/connectors/checkpoint_connector.py around checkpoint path parsing. Reproduce with a cloud logger save directory, ModelCheckpoint without dirpath, and ckpt_path="last". Done means the cloud filesystem is selected and the checkpoint retains its protocol so resuming finds last.ckpt.

Written by the indexing model from the issue text.