Error learning rate when load ckpt for cotinue training if check_val_every_n_epoch > 1

Nobody has claimed this yet.

### Bug description

I run with LightningCLI. Set check_val_every_n_epoch > 1 (e.g. 2) to run an experiment with 20 max_epoches, the model ckpt is save by lightning.pytorch.callbacks.ModelCheckpoint. The learning rate schedular is torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=self.trainer.max_epochs, T_mult=1, eta_min=self.eta_min) and update each epoch. When I load a ckpt (e.g. saved at epoch 3) to continue training, the learning rate will update 1 epoch quicker than expected.
Here the red is original learning rate curve and the yellow is the continued. The lr is logged by lightning.pytorch.callbacks.LearningRateMonitor.

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

v2.4

### How to reproduce the bug

```yaml
the trainer setting
seed_everything: 0
trainer:
accelerator: gpu
strategy: auto
devices:
- 0
num_nodes: 1
precision: 32-true
logger:
class_path: lightning.pytorch.loggers.TensorBoardLogger
init_args:
save_dir: runs
name: name
version: null
log_graph: false
default_hp_metric: true
prefix: ''
sub_dir: test
callbacks:
- class_path: utils.log_manager.LogManager
- class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
dirpath: null
filename: 'epoch={epoch}-psnr={hp_metric:.4f}'
monitor: hp_metric
verbose: false
save_last: true
save_top_k: 5
save_weights_only: false
mode: max
auto_insert_metric_name: false
every_n_train_steps: null
train_time_interval: null
every_n_epochs: 1
save_on_train_epoch_end: null
fast_dev_run: false
max_epochs: 20
min_epochs: null
max_steps: -1
min_steps: null
max_time: null
limit_train_batches: null
limit_val_batches: null
limit_test_batches: null
limit_predict_batches: null
overfit_batches: 0.0
val_check_interval: null
check_val_every_n_epoch: 2
num_sanity_val_steps: null
log_every_n_steps: 20
enable_checkpointing: null
enable_progress_bar: null
enable_model_summary: null
accumulate_grad_batches: 1
gradient_clip_val: null
gradient_clip_algorithm: null
deterministic: null
benchmark: null
inference_mode: true
use_distributed_sampler: true
profiler: null
detect_anomaly: false
barebones: false
plugins: null
sync_batchnorm: false
reload_dataloaders_every_n_epochs: 0
default_root_dir: null
```
```py
def configure_optimizers(self):
params = []
params.append({'params': self.network.parameters(), 'lr': self.lr, 'weight_decay': self.weight_decay, 'name': 'network'})
optimizer = torch.optim.AdamW(params)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=self.trainer.max_epochs, T_mult=1, eta_min=self.eta_min)
lr_scheduler_config = {
"scheduler": lr_scheduler,
"interval": 'epoch',
"name": 'AdamW'
}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config}
```

### 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_

cc @lantiga

Start with the provided configure_optimizers scheduler configuration and the Trainer resume flow using ModelCheckpoint; reproduce with check_val_every_n_epoch: 2 and LearningRateMonitor. Compare scheduler state and epoch progression before and after loading the epoch-3 checkpoint, with the fix validated when continued learning-rate curves align with uninterrupted training.

Written by the indexing model from the issue text.