The label column can also be chosen as an input column, and nothing says so

### What happened?

Two families of sklearn operators let the column already chosen as the label be chosen a second time as an input column, and neither says anything while the workflow is being written.

*Text Attribute, on the fifty-one operators of the Sklearn and Sklearn Training groups.* `Target Attribute` names the label and `Text Attribute` names the columns `Count Vectorizer` tokenizes. Both dropdowns offer every column of the input, so one column can be picked for both. The generated code drops the target before the pipeline reads its columns (`SklearnClassifierOpDesc.scala:44`, `X = table.drop(target, axis=1)`), so the `ColumnTransformer` asks for a column that is no longer there and the run ends with `ValueError: A given column is not a column of the dataframe`, naming neither of the two fields the user has to change. Failing is right here, since vectorizing the label would train a model on the answer. What is missing is refusing the configuration in the panel.

*Selected Features, on the four advanced trainers* (KNN Classifier, KNN Regressor, SVC, SVR). `Ground Truth Attribute Column` names the label and `Selected Features` names the columns handed to `fit`. The same overlap is reachable, and here nothing fails at all: the generated code is `X_train = self.dataset[features]` with the label among `features` (`SklearnAdvancedBaseDesc.scala:120-121`), so the estimator is given the answer as an input. On two hundred rows whose label is random noise, a KNN classifier scores 0.685 on the two real features and 0.995 once the label is included. Nothing in the run says the label was among the features.

Expected: a column already chosen as the label is refused as an input column, with a message naming both fields. `SklearnModelOpDesc.getOutputSchemas` already refuses one impossible pairing this way, the `Count Vectorizer` switch on the two Gaussian Naive Bayes operators, so the shared base has a place for it. The advanced trainers have no such check today.

### How to reproduce?

For Text Attribute, upload a CSV with a text column and a label:

```
note,species
great excellent good,1
awful terrible bad,0
```

Build `CSV File Scan` to `Bernoulli Naive Bayes`, connect the scan to both the training and the testing port, set Target Attribute to `species`, turn Count Vectorizer on, and set Text Attribute to `species` as well. The panel accepts it. The run ends with the ValueError above.

For Selected Features, upload a CSV with two numeric columns and a label, build `CSV File Scan` to `KNN Classifier`, connect the scan to both ports, set Ground Truth Attribute Column to `species`, set Selected Features to all three columns including `species`, and add one parameter row with `n_neighbors` = `5`. The panel accepts it and the run finishes clean, having trained on the label.

### Version/Branch

1.3.0-incubating-SNAPSHOT (main)

### Commit Hash (Optional)

319859571