`type_of_target` returns `unknown` for valid arrays of dtype `object`

### Describe the bug

`sklearn.utils.multiclass.type_of_target` returns unknown for arrays of integers if they have a dtype of `object`, when it should instead return a valid type.

I would be happy to contribute a fix, but I'm not entirely sure how the failing condition should be corrected:
https://github.com/scikit-learn/scikit-learn/blob/45756377c748d84aa52f66950b8d9eeefc31456c/sklearn/utils/multiclass.py#L317-L319

### Steps/Code to Reproduce

```python
import pandas as pd
from sklearn.utils.multiclass import type_of_target

series = pd.Series(['a', 'b'])
series.loc[series == 'a'] = 0
series.loc[series == 'b'] = 1

print(series)
print(type_of_target(series))
print(type_of_target(list(series)))
```
Output is `unknown` whereas once converted to a list, the output is `binary` as expected.

### Expected Results

`type_of_target` returns a valid type (`binary`).

### Actual Results

This issue resulted in enigmatic errors when using dependent functions such as `confusion_matrix`, e.g.:
```
Traceback (most recent call last):
  File "test.py", line 214, in <module>
    cm = confusion_matrix(y_test, prediction_classes)
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 307, in confusion_matrix
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 93, in _check_targets
    raise ValueError(
ValueError: Classification metrics can't handle a mix of unknown and binary targets
```

### Versions

```shell
System:
    python: 3.8.0 (default, Oct  6 2020, 12:20:13)  [GCC 6.5.0 20181026]
executable: ~/.pyenv/versions/wat/bin/python3
   machine: Linux-4.15.0-132-generic-x86_64-with-glibc2.27

Python dependencies:
      sklearn: 1.1.2
          pip: 22.0.4
   setuptools: 58.1.0
        numpy: 1.23.2
        scipy: 1.6.0
       Cython: 0.29.21
       pandas: 1.1.3
   matplotlib: 3.3.3
       joblib: 1.0.0
threadpoolctl: 2.1.0

Built with OpenMP: True
Traceback (most recent call last):
  File "_ctypes/callbacks.c", line 237, in 'calling callback function'
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/threadpoolctl.py", line 400, in match_module_callback
    self._make_module_from_path(filepath)
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/threadpoolctl.py", line 515, in _make_module_from_path
    module = module_class(filepath, prefix, user_api, internal_api)
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/threadpoolctl.py", line 606, in __init__
    self.version = self.get_version()
  File "~/.pyenv/versions/wat/lib/python3.8/site-packages/threadpoolctl.py", line 646, in get_version
    config = get_config().split()
AttributeError: 'NoneType' object has no attribute 'split'

threadpoolctl info:
       filepath: ~/.pyenv/versions/3.8.0/envs/wat/lib/python3.8/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
         prefix: libgomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 8

       filepath: ~/.pyenv/versions/3.8.0/envs/wat/lib/python3.8/site-packages/scipy.libs/libopenblasp-r0-085ca80a.3.9.so
         prefix: libopenblas
       user_api: blas
   internal_api: openblas
        version: 0.3.9
    num_threads: 8
threading_layer: pthreads
```

COMMENT:
> Hmm, perhaps. I understand it's not the greatest way of doing things, but this wasn't my code - I used the following example (https://github.com/serengil/tensorflow-101/blob/master/python/Ensemble-Face-Recognition.ipynb), where the initial decision (target) values ("No", "Yes") are changed to (0, 1) for training, definitely a conceivable use-case.

Oh I forgot this part of my answer. Indeed, you can pass directly the string to a scikit-learn classifier because it will be internally encoded. The second possibility is to use the `LabelEncoder` that will properly do the transform intended (and to invert the transform as well).

Labels: Enhancement, module:utils