Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification

### Describe the bug

I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:

```pytb
ValueError: pos_label=1 is not a valid label: It should be one of ['setosa' 'versicolor' 'virginica']
```

According to the documentation, the `pos_label` argument should be ignored for the multiclass problem:

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#f1-score

_The class to report if `average='binary'` and the data is binary, otherwise this parameter is ignored._

Setting `pos_label` explicitly to None solves the problem and produces the expected output, see below.

### Steps/Code to Reproduce

```python
# Import necessary libraries
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import make_scorer, f1_score

# Load the Iris dataset
data = load_iris()
X = data.data  # Features
y = data.target  # Labels

# Convert labels to string type
y = np.array([data.target_names[label] for label in data.target])

# Initialize the Linear Discriminant Analysis classifier
classifier = LinearDiscriminantAnalysis()

# Define a custom scorer using F1 score with average='micro'
f1_scorer = make_scorer(f1_score, average='micro', pos_label=1)

# Perform cross-validation with cross_val_score
try:
    scores = cross_val_score(classifier, X, y, cv=5, scoring=f1_scorer)
    print(f"Cross-validated F1 Scores (micro average): {scores}")
    print(f"Mean F1 Score: {np.mean(scores)}")
except ValueError as e:
    print(f"Error: {e}")
```

### Expected Results

```
Cross-validated F1 Scores (micro average): [1.         1.         0.96666667 0.93333333 1.        ]
Mean F1 Score: 0.9800000000000001
```

### Actual Results

```pytb
Cross-validated F1 Scores (micro average): [nan nan nan nan nan]
Mean F1 Score: nan
[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:1000](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/model_selection/_validation.py#line=999): UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 139](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=138), in __call__
    score = scorer._score(
            ^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 371](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=370), in _score
    y_pred = method_caller(
             ^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 89](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=88), in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 204](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/utils/_response.py#line=203), in _get_response_values
    raise ValueError(
ValueError: pos_label=1 is not a valid label: It should be one of ['setosa' 'versicolor' 'virginica']
```

### Versions

```shell
System:
    python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\rgt0227\AppData\Local\anaconda3\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.5.0
          pip: 23.2.1
   setuptools: 68.0.0
        numpy: 1.26.2
        scipy: 1.11.4
       Cython: None
       pandas: 2.1.1
   matplotlib: 3.8.0
       joblib: 1.2.0
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: mkl
    num_threads: 8
         prefix: mkl_rt
       filepath: C:\Users\rgt0227\AppData\Local\anaconda3\Library\bin\mkl_rt.2.dll
        version: 2023.1-Product
threading_layer: intel

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: vcomp
       filepath: C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\.libs\vcomp140.dll
        version: None
```

COMMENT:
Ah, this helps. Thanks for the clarification!

### Bug-throwing code:
I have also encountered this bug while using scikit-learn 1.4.2. 
I am evaluating multiple scores by assigning a dictionary to the scoring parameter like so:

```Python
scoring = {
    'precision': make_scorer(precision_score, average='macro', zero_division=0),
    'recall': make_scorer(recall_score, average='macro', zero_division=0),
    'f1': make_scorer(f1_score, average='macro', zero_division=0)
}

results = cross_validate(model, X, y, cv=cv, scoring=scoring)
```

### Working Dicts
My code was only showing errors after I removed `balanced_accuracy_score`, which was my leading score until then (calculating the same value as the macro recall). Apparently, **using multiple scorers and having a "working" one in the first position also fixes every scorer that comes after.**

Alternatively, it is also enough to assign `pos_label=None` to just the first scorer.

```Python
scoring = {
    'accuracy': make_scorer(balanced_accuracy_score),
    'precision': make_scorer(precision_score, average='macro', zero_division=0),
    'recall': make_scorer(recall_score, average='macro', zero_division=0),
    'f1': make_scorer(f1_score, average='macro', zero_division=0)
}
```
or
```
scoring = {
    'precision': make_scorer(precision_score, average='macro', pos_label=None, zero_division=0),
    'recall': make_scorer(recall_score, average='macro', zero_division=0),
    'f1': make_scorer(f1_score, average='macro', zero_division=0)
}
```

Labels: Bug