Metrics get mapped twice to the same epoch in MLflow logger

Nobody has claimed this yet.

### Bug description

The MLFlow logger associates two consecutive values of a metric with one epoch in the metric plots in the MLFlow ui.
This does not happen when x-axis is `steps`.

I'm logging my metrics by using `on_step=False` and `on_epoch=True` using the following code:

```
def training_step(self, batch: torch.Tensor, batch_idx: int):
outdict = self.model_step(batch.flatten(start_dim=1).to(dtype=torch.float32))

# Decide what to log:
self.log_dict(
self._log_dict(outdict, "train", dataloader_idx=0),
prog_bar=False,
on_step=False,
on_epoch=True,
logger=True,
sync_dist=True,
add_dataloader_idx=False,
)
return outdict

def validation_step(
self, batch: torch.Tensor, batch_idx: int, dataloader_idx: Optional[int] = 0
):
outdict = self.model_step(batch.flatten(start_dim=1).to(dtype=torch.float32))
self.log_dict(
self._log_dict(outdict, "val", dataloader_idx=dataloader_idx),
prog_bar=False,
on_step=False,
on_epoch=True,
logger=True,
sync_dist=True,
add_dataloader_idx=False,
)

return outdict
```
I'm not calling `log_metric` or `log_dict` anywhere else.

The plots look like this when having `step` on the x-axis.

The same plot using `epoch` on the x-axis.

The epoch vs step plot looks fine though

Ignore the last point, that is due to running on the test data. Even if I don't run on this data, I still get the 'double points' for each epoch.

Am I doing something wrong? This seems like a pretty basic use case.

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

v2.5

### Reproduced in studio

_No response_

### How to reproduce the bug

```python

```

### Error messages and logs

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

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.5.0):
#- 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 @lantiga @borda

Start by reproducing the behavior with the shown training_step and log_dict configuration, focusing on the MLflow logger and epoch-based metric mapping. Inspect the logger path handling on_epoch=True and compare it with steps-axis logging; done when each epoch produces one metric point without changing the correct steps-axis behavior.

Written by the indexing model from the issue text.