`test_unsorted_indices` for `SVC` may fail randomly with sparse vs dense data

### Describe the bug

The [<code>test_unsorted_indices</code>](https://github.com/scikit-learn/scikit-learn/blob/cfd5f7833dfb3794e711e79e4a3373e599d5a1f0/sklearn/svm/tests/test_sparse.py#L121) function occasionally fails on CI when comparing the coefficients of `SVC(kernel="linear", probability=True, random_state=0)` trained on dense vs sparse data.

I suspect this is due to additional randomness introduced by the internal cross-validation and Platt scaling when `probability=True` is set. See the [SVC documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) for reference.

### Steps/Code to Reproduce

Unfortunately, I haven't been able to reproduce the failure reliably. I've only seen it fail three times when creating or reviewing PRs, but the error disappears after re-running CI.

I've also tried looping through various `random_state` values without triggering a failure locally.

For now, I'm labelling this with "Hard" and "Needs Reproducible Code."

### Expected Results

```python
def test_unsorted_indices(csr_container):
    # test that the result with sorted and unsorted indices in csr is the same
    # we use a subset of digits as iris, blobs or make_classification didn't
    # show the problem
    X, y = load_digits(return_X_y=True)
    X_test = csr_container(X[50:100])
    X, y = X[:50], y[:50]
    tols = dict(rtol=1e-12, atol=1e-14)

    X_sparse = csr_container(X)
    coef_dense = (
        svm.SVC(kernel="linear", probability=True, random_state=0).fit(X, y).coef_
    )
    sparse_svc = svm.SVC(kernel="linear", probability=True, random_state=0).fit(
        X_sparse, y
    )
    coef_sorted = sparse_svc.coef_
    # make sure dense and sparse SVM give the same result
    assert_allclose(coef_dense, coef_sorted.toarray(), **tols)
```
should consistently pass.

### Actual Results

In rare cases, the assertion fails:

```console
AssertionError: 
Not equal to tolerance rtol=1e-07, atol=0       
Mismatched elements: 2 / 2880 (0.0694%)
Max absolute difference among violations: 3.46944695e-18
Max relative difference among violations: inf
ACTUAL: array([[ 0.      ,  0.      ,  0.001342, ..., -0.002487,  0.      ,
        0.      ],
      [ 0.      ,  0.      , -0.000967, ..., -0.010943, -0.012946,...
DESIRED: array([[ 0.      ,  0.      ,  0.001342, ..., -0.002487,  0.      ,
        0.      ],
      [ 0.      ,  0.      , -0.000967, ..., -0.010943, -0.012946,...
```

However, re-running CI causes the test to pass. This suggests the failure is non-deterministic, likely comming from subtle differences in how sparse vs dense inputs interact with the internal cross-validation and Platt scaling.

### Versions

```shell
System:
    python: 3.12.6 (tags/v3.12.6:a4a2d2b, Sep  6 2024, 20:11:23) [MSC v.1940 64 bit (AMD64)]
executable: D:\PycharmProjects\scikit-learn\venv1\Scripts\python.exe
   machine: Windows-11-10.0.22621-SP0

Python dependencies:
      sklearn: 1.8.dev0
          pip: 25.0.1
   setuptools: 70.2.0
        numpy: 2.2.3
        scipy: 1.15.2
       Cython: 3.0.12
       pandas: 2.2.3
   matplotlib: 3.10.0
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 20
         prefix: libscipy_openblas
       filepath: D:\PycharmProjects\scikit-learn\venv1\Lib\site-packages\numpy.libs\libscipy_openblas64_-43e11ff0749b8cbe0a615c9cf6737e0e.dll
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: blas
   internal_api: openblas
    num_threads: 20
         prefix: libscipy_openblas
       filepath: D:\PycharmProjects\scikit-learn\venv1\Lib\site-packages\scipy.libs\libscipy_openblas-f07f5a5d207a3a47104dca54d6d0c86a.dll
        version: 0.3.28
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 20
         prefix: vcomp
       filepath: C:\Windows\System32\vcomp140.dll
        version: None
```

COMMENT:
Indeed if we're comparing against True zeros we need an atol.

Labels: Bug, Hard, module:svm, Needs Reproducible Code