Add `ref_line_kwargs` to CalibrationDisplay

### Describe the workflow you want to enable

**I would like to style the `CalibrationDisplay` reference line by passing styling arguments  via a kwargs object.**

If I need the reference line colored `'white'`, I would like to enable the following workflow:
```python
fig, ax = plt.subplots()
CalibrationDisplay.from_estimator(model, X_test, y_test, ax=ax , ref_line_kwargs={'color':'white'})
```

### Describe your proposed solution

#### 1) Add a `ref_line_kwargs` parameter to the `CalibrationDisplay.plot` method. ([Line 1092](https://github.com/scikit-learn/scikit-learn/blob/baf0ea25d6dd034403370fea552b21a6776bef18/sklearn/calibration.py#L1092))
```python
def plot(self, *, ax=None, name=None, ref_line=True, ref_line_kwargs={}, **kwargs):
```

#### 2) Pass the `ref_line_kwargs` object into `ax.plot`. ([Line 1139](https://github.com/scikit-learn/scikit-learn/blob/baf0ea25d6dd034403370fea552b21a6776bef18/sklearn/calibration.py#L1139))
```python
ax.plot([0, 1], [0, 1], "k:", label=ref_line_label, **ref_line_kwargs)
```

### Describe alternatives you've considered, if relevant

#### There are two alternatives that allow styling the reference line
1. Add a reference line manually and set `ref_line=False`
2. Add a reference line manually and set the label for the line to `'Perfectly calibrated'`

#### 1)
```python
fig, ax = plt.subplots()
ax.plot([0, 1], [0, 1], "k:", color='white')
CalibrationDisplay.from_estimator(model, X_test, y_test, ref_line=False, ax=ax)
```

#### 2)
```python
fig, ax = plt.subplots()
ax.plot([0, 1], [0, 1], "k:", label='Perfectly calibrated', color='white')
CalibrationDisplay.from_estimator(model, X_test, y_test, ax=ax)
```

### Additional context

For my current project, to generate a white reference line, I found myself copying and pasting the source code used for plotting the reference line by sklearn, and then adding the styling edits I needed. 

The primary benefit of this change would be the ability to style all of the plotted data without needing to interact with the matplotlib api. 

There appears to be an active pull request for improving the calibration plotting tools, and given the simplicity of this change, and if others agree with the request, #23748 seems like a natural place for this update.

COMMENT:
As for adding `ref_line_kwargs`, the API for the display objects were designed to expose the matplotlib artist, so you can make edits after the fact. In this case, the simplest change is to define a `disp.ref_line_` and allow one to make the edit. Something like this:

```python
# Note this does not work yet, since `ref_line_` is not defined:
disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
disp.ref_line_.set_color("white")
```

Note that [PartialDependenceDisplay.from_estimator](https://scikit-learn.org/stable/modules/generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.from_estimator) where we have `line_kw` because that visualization has to configure multiple lines.

We can not do `**ref_line_kwargs`, because it will make it harder to add a `line_kwargs` in the future adjusting the calibration curve. Overall, I am okay with adding `ref_line_kw` as it follows the same style as other `Display` objects.

> if others agree with the request, https://github.com/scikit-learn/scikit-learn/pull/23748 seems like a natural place for this update.

If this feature is accepted, then a separate PR is best. This reduces the scope of each of the PRs which makes it easier to review and merge.

Labels: New Feature, module:calibration, Needs Decision - Include Feature