Lasso incompatible with scipy=1.11 with sparse X

### Describe the bug

The Lasso() regressor seems to be incompatible with the newest release of scipy=1.11.0 with sparse X input. 


### Steps/Code to Reproduce

```
import numpy as np
from scipy.sparse import csc_array
from sklearn.linear_model import Lasso

if __name__ == '__main__':
    # Create sparse matrix
    X = csc_array(np.array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))  # also fails with dok_array
    y = np.array([1, 2, 3])

    lasso = Lasso(fit_intercept=True)  # fit_intercept=True gets a more intentionally but still unexpected TypeError, with False it looks like a bug.
    lasso.fit(X, y)     # Fit Lasso to show that it fails. LinearRegression and Ridge work fine.
    print('Done!')
```

### Expected Results

No error is thrown.

### Actual Results

With fit_intercept=False, it fails with a strange TypeError which looks like it's not intended. With fit_intercept=True, it fails with a TypeError saying it wants a csc array, which is provided though. This seems to be an inconsistency with the latest scipy version and should be considered in the conda installation if it turns out to be an unintended bug.

### Versions

```shell
python=3.9.5
sklearn=1.3.0
scipy=1.11.0 fails while 1.10 is fine
```

COMMENT:
I cannot reproduce on `main`. Could you provide the full traceback as well.

```python
In [2]: import numpy as np
   ...: from scipy.sparse import csc_array
   ...: from sklearn.linear_model import Lasso
   ...: 
   ...: # Create sparse matrix
   ...: X = csc_array(np.array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))  # also fails with dok_array
   ...: y = np.array([1, 2, 3])
   ...: 
   ...: lasso = Lasso(fit_intercept=True)  # fit_intercept=True gets a more intentionally but still unexpected TypeError, with False it looks
   ...:  like a bug.
   ...: lasso.fit(X, y)     # Fit Lasso to show that it fails. LinearRegression and Ridge work fine.
   ...: print('Done!')

Done!

In [3]: 

In [3]: import numpy as np
   ...: from scipy.sparse import csc_array
   ...: from sklearn.linear_model import Lasso
   ...: 
   ...: # Create sparse matrix
   ...: X = csc_array(np.array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))  # also fails with dok_array
   ...: y = np.array([1, 2, 3])
   ...: 
   ...: lasso = Lasso(fit_intercept=False)  # fit_intercept=True gets a more intentionally but still unexpected TypeError, with False it look
   ...: s like a bug.
   ...: lasso.fit(X, y)     # Fit Lasso to show that it fails. LinearRegression and Ridge work fine.
   ...: print('Done!')
Done!
```

```
In [5]: sklearn.show_versions()

System:
    python: 3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:25:29) [Clang 14.0.6 ]
executable: /Users/glemaitre/mambaforge/envs/dev/bin/python3.10
   machine: macOS-13.5-arm64-arm-64bit

Python dependencies:
      sklearn: 1.4.dev0
          pip: 22.3.1
   setuptools: 65.5.1
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: 0.29.33
       pandas: 2.0.3
   matplotlib: 3.7.1
       joblib: 1.3.0.dev0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libopenblas.0.dylib
        version: 0.3.21
threading_layer: openmp
   architecture: VORTEX
    num_threads: 8

       user_api: openmp
   internal_api: openmp
         prefix: libomp
       filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libomp.dylib
        version: None
    num_threads: 8
```

Labels: Bug, Needs Triage