Unexpected behavior of the HTML repr of meta-estimators

Here's a list of some unexpected behaviors of the HTML repr of meta-estimators
- `Pipeline` doesn't display its named steps

  <img width="164" height="95" alt="Image" src="https://github.com/user-attachments/assets/c368188d-544f-4bb1-bfeb-d76490f5146a" />

  This was maybe intentional ? In comparison `FeatureUnion` does for instance

  <img width="282" height="91" alt="Image" src="https://github.com/user-attachments/assets/2a6ded8c-d148-4852-a03c-0a88c1b8bc49" />

- a `Pipeline` in a meta-estimator doesn't render properly; it doesn't have the dashed border
  
  <img width="186" height="120" alt="Image" src="https://github.com/user-attachments/assets/471ce468-8597-4fbc-b718-870c4f11a1fd" />

  Another meta-estimator renders properly

  <img width="294" height="116" alt="Image" src="https://github.com/user-attachments/assets/cb467dcd-63c2-4a54-b92a-d6686b3b3bc8" />

- transformers of `ColumnTransformer` are expandable to show the selected columns, but there's no additional info. I think it should be explicit that these are the selected columns.

  <img width="274" height="106" alt="Image" src="https://github.com/user-attachments/assets/e44d47fb-c784-4631-9a73-3d274671430e" />

  Note that this could be fixed by https://github.com/scikit-learn/scikit-learn/pull/31937

- When the inner estimator of a meta-estimator is not a meta-estimator itself, it's expandable but the dropdown is not useful anymore (it's the non-html repr of the estimator basically):

  <img width="169" height="129" alt="Image" src="https://github.com/user-attachments/assets/21723076-ae7e-46e3-8d22-3561aef1b1ec" />

  Now that we have the parameter table, it's not useful anymore to have the additional repr which is redundant and less informative. I'd be in favor of removing the dropdown

- When the inner estimator of a meta-estimator is a meta-estimator itself, it's expandable but sometimes there's no dropdown or the dropdown is the non-html repr of the meta-estimator, and there's no parameter table.

  <img width="178" height="121" alt="Image" src="https://github.com/user-attachments/assets/4fbdb1fa-8f5e-4d32-9bd2-56bc4e89d891" />

  <img width="392" height="143" alt="Image" src="https://github.com/user-attachments/assets/1294de22-afcb-49c3-9256-b0cda06f352a" />

  I think that it should display the parameter table instead in both cases

COMMENT:
In https://github.com/probabl-ai/skore/issues/2068, we detected either a bug (not sure it is a regression). With a column transformer, it seems that we don't properly handle the `remainder` parameter. For instance, let's take the following example:

```python
from sklearn.cluster import KMeans
from sklearn.compose import make_column_transformer
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline

cols = ["xxx"]
preprocessor = make_column_transformer(
    (KMeans(), geo_columns), (KMeans(), geo_columns)
)
estimator = make_pipeline(preprocessor,Ridge())
estimator
```

<img width="420" height="220" alt="Image" src="https://github.com/user-attachments/assets/9e2fa5cb-aa2d-4b00-99cf-d05a8aa5d384" />

Everything works as expected. However if we set the remainder:

```python
from sklearn.cluster import KMeans
from sklearn.compose import make_column_transformer
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline

cols = ["xxx"]
preprocessor = make_column_transformer(
    (KMeans(), geo_columns), (KMeans(), geo_columns), remainder="passthrough"
)
estimator = make_pipeline(preprocessor,Ridge())
estimator
```

<img width="495" height="219" alt="Image" src="https://github.com/user-attachments/assets/37cc7eb1-d0e1-4657-80e5-af8e325ba9f7" />

The inner dotted line for the `ColumnTransformer` is not right. It is extended beyond the `ColumnTransformer` and make that the next step of the `Pipeline` is included in the `ColumnTransformer`.

To make it more difficult, it seems that the problem happens in the notebook but not in the documentation, e.g. https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#processing-the-dataset

```python
import scipy as sp
from sklearn.compose import TransformedTargetRegressor
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import OneHotEncoder

categorical_columns = ["RACE", "OCCUPATION", "SECTOR", "MARR", "UNION", "SEX", "SOUTH"]
numerical_columns = ["EDUCATION", "EXPERIENCE", "AGE"]

preprocessor = make_column_transformer(
    (OneHotEncoder(drop="if_binary"), categorical_columns),
    remainder="passthrough",
    verbose_feature_names_out=False,  # avoid to prepend the preprocessor names
)

model = make_pipeline(
    preprocessor,
    TransformedTargetRegressor(
        regressor=Ridge(alpha=1e-10), func=np.log10, inverse_func=sp.special.exp10
    ),
)
model
```

<img width="658" height="349" alt="Image" src="https://github.com/user-attachments/assets/d8d582b3-dcd2-4d89-ac67-6782d2c37eeb" />

And I get in a notebook:

<img width="589" height="304" alt="Image" src="https://github.com/user-attachments/assets/e39fea19-9c49-4690-a3cc-b1158ed29a83" />

Labels: Documentation, frontend