CI : Numerical flakiness in test_enet_ols_consistency

### Describe the bug

While working on another scikit-learn PR (**#32368**), CI intermittently failed in `sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency`.It came from a strict numerical ordering assertion on the squared error:

```python
assert se_enet <= se_ols <= 1e-20
```

This was recently merged 2 days ago in #32845 . On further digging, I noticed:

* the squared error is computed **without `sample_weight`**, even though both models are fit with `sample_weight`
* Lot of tests in this test file are using a **hard-coded RNG seed** (`RandomState(42)`) instead of `global_random_seed` ( I did have a look at #22827 ) 
* when running across multiple seeds (e.g. with `SKLEARN_TESTS_GLOBAL_RANDOM_SEED=all`), additional failures can appear for some configurations due to **tight absolute tolerances** on `intercept_` comparisons near zero.

P.S Same test was passing for seed 42 on Windows locally. However as i start changing seeds it would fail on Windows too. 


### Steps/Code to Reproduce

I updated this test to use `global_random_seed` and ran it for all seeds on Ubuntu. 

```bash
SKLEARN_TESTS_GLOBAL_RANDOM_SEED=all pytest sklearn/linear_model/tests/test_coordinate_descent.py -k test_enet_ols_consistency
```


```python
@pytest.mark.filterwarnings("ignore:With alpha=0, this algorithm:UserWarning")
@pytest.mark.parametrize("precompute", [False, True])
@pytest.mark.parametrize("effective_rank", [None, 10])
def test_enet_ols_consistency(precompute, effective_rank,global_random_seed):
    """Test that ElasticNet(alpha=0) converges to the same solution as OLS."""
    rng = np.random.RandomState(global_random_seed)
    n_samples = 300
    X, y = make_regression(
        n_samples=n_samples,
        n_features=100,
        effective_rank=effective_rank,
        n_informative=50,
        random_state=rng,
    )
    sw = rng.uniform(low=0.01, high=10, size=X.shape[0])

    ols = LinearRegression().fit(X, y, sample_weight=sw)
    enet = ElasticNet(alpha=0, precompute=precompute, tol=1e-15).fit(
        X, y, sample_weight=sw
    )

    # Might be a singular problem, so check for same predictions
    assert_allclose(enet.predict(X), ols.predict(X))
    # and for similar objective function (squared error)
    se_ols = np.sum((y - ols.predict(X)) ** 2)
    se_enet = np.sum((y - enet.predict(X)) ** 2)
    if precompute:
        assert se_ols <= 1e-20
        assert se_enet <= 1e-20
    else:
        assert se_enet <= se_ols <= 1e-20  # Who would have thought that?
    # We check equal coefficients, but "only" with absolute tolerance.
    assert_allclose(enet.coef_, ols.coef_, atol=1e-11)
    assert_allclose(enet.intercept_, ols.intercept_, atol=1e-12)
```


### Expected Results

No test failures

### Actual Results

Reference CI failure (exact run):

```text
https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=83843&view=ms.vss-test-web.build-test-results-tab&runId=837146&resultId=115395&paneView=debug
```

For  my local run, 

```bash
FAILED sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency[42-10-False] - assert 7.304604230718523e-26 <= 6.721669301829635e-26
FAILED sklearn/linear_model/tests/test_coordinate_descent.py::test_enet_ols_consistency[59-None-True] - AssertionError:
```

### Versions

```shell
System:
    python: 3.11.14 | packaged by conda-forge | (main, Oct 22 2025, 22:46:25) [GCC 14.3.0]
executable: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/bin/python3
   machine: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.9.dev0
          pip: 25.3
   setuptools: 80.9.0
        numpy: 1.24.1
        scipy: 1.10.0
       Cython: 3.1.2
       pandas: 1.5.0
   matplotlib: 3.6.1
       joblib: 1.3.0
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 32
         prefix: libopenblas
       filepath: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/lib/libopenblasp-r0.3.25.so
        version: 0.3.25
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 32
         prefix: libomp
       filepath: /home/gauta/miniforge3/envs/scikit-learn-min-dependencies/lib/libomp.so
        version: None
```

COMMENT:
Hi all, 

tysm for the quick fix :) Do you think it makes sense to update all the other tests to use global random seed for this file just so that we dont run into any future issues? We can also update the corresponding tracker on #22827. If yes, i can have it done and create a quick PR. 

CC : @OmarManzoor , @lorentzenchr , @ogrisel

Labels: Bug, module:linear_model, module:test-suite