TensorBoardLogger has the wrong epoch numbers much more than the fact

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### Bug description

I used the following code to log the metrics, but I found that the epoch recorded in the tensorboard logger is much more than it should have:

def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self.forward(x)
loss = torch.sqrt(self.loss_fn(y_hat,y))
self.log("train_loss", loss, logger=True, prog_bar=True, on_epoch=True)
return loss

def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.forward(x)
loss = torch.sqrt(self.loss_fn(y_hat,y))
self.log("valid_loss", loss, logger=True, prog_bar=True, on_epoch=True)
return loss

pl.Train(..., logger=TensorBoardLogger(save_dir='store',version=log_path), ....)

In the configure, I set max_epoch=10000, but in the logger, I got epoches more than 650k:

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

v2.1

### How to reproduce the bug

```python
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self.forward(x)
loss = torch.sqrt(self.loss_fn(y_hat,y))
self.log("train_loss", loss, logger=True, prog_bar=True, on_epoch=True)
return loss

def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.forward(x)
loss = torch.sqrt(self.loss_fn(y_hat,y))
self.log("valid_loss", loss, logger=True, prog_bar=True, on_epoch=True)
return loss

pl.Train(..., logger=TensorBoardLogger(save_dir='store',version=log_path), ....) # u can use any path you like
```

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0): 2.1.3
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9): 2.1.2
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source): pip
#- Running environment of LightningApp (e.g. local, cloud):
```

### More info

_No response_

cc @lantiga

The report provides no repository file or test to follow. Start by reproducing the shown training_step and validation_step with TensorBoardLogger, then compare the configured max_epoch with the epoch or step values written to TensorBoard. Done means the logger reports the expected epoch range and a regression test covers the behavior.

Written by the indexing model from the issue text.