Restoring Trainer State with Early Stop fails

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## 🐛 Bug

Whenever I try to restore the state of a previous (broken) run and I have Early Stop configured, I got the following error:

```
RuntimeError: Early stopping conditioned on metric `val/f1/default` which is not available. Pass in or modify your `EarlyStopping` callback to use any of the following: ``
```

It seems the metrics are not saved together with the Checkpoint data and Early Stop tries to be applied straight away.

### To Reproduce

1. Start training a model with Checkpointing (best and last) and Early Stop on a validation metric. I have used Pytorch-Lightning Cli for this.
2. The model crashes after some checkpoints were saved
3. Try to re-run the training passing `ckpt_path` with the last checkpoint path to the trainer (with the same Early Stopping settings).
4. The error happens.

### Expected behavior

The model should resume training, knowing the last best value, and apply Early Stopping as appropriate.

### Environment

* CUDA:
- GPU:
- NVIDIA A100-SXM4-80GB
- available: True
- version: 11.4
* Packages:
- numpy: 1.21.2
- pyTorch_debug: False
- pyTorch_version: 1.10.0a0+0aef44c
- pytorch-lightning: 1.6.1
- tqdm: 4.62.3
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.8.12
- version: #103-Ubuntu SMP Fri Nov 26 16:13:00 UTC 2021

### Additional context

Running using the Pytorch Lightning Cli

cc @carmocca @awaelchli @rohitgr7

Start by reproducing the failure with checkpoint restoration, Early Stopping, and a validation metric such as `val/f1/default`, then inspect how checkpoint state and EarlyStopping state are restored. Confirm the fix by resuming from the last checkpoint after a crash and verifying that training continues and Early Stopping uses the restored best value.

Written by the indexing model from the issue text.