DBSCAN always triggers and EfficiencyWarning

### Describe the bug

Calling dbscan always triggers an efficiency warning. There is no apparent way to either call it correctly or disable the warning. 

This was originally reported as an issue in SemiBin, which uses DBSCAN under the hood: https://github.com/BigDataBiology/SemiBin/issues/175

### Steps/Code to Reproduce

```python
import numpy as np
from sklearn.cluster import dbscan
from sklearn.neighbors import kneighbors_graph, sort_graph_by_row_values

f = np.random.randn(10_000, 240)
dist_matrix = kneighbors_graph(
    f,
    n_neighbors=200,
    mode='distance',
    p=2,
    n_jobs=3)

_, labels = dbscan(dist_matrix,
        eps=0.1, min_samples=5, n_jobs=4, metric='precomputed')


dist_matrix = sort_graph_by_row_values(dist_matrix)
_, labels = dbscan(dist_matrix,
        eps=0.1, min_samples=5, n_jobs=4, metric='precomputed')
```

### Expected Results

No warning, at least in second call

### Actual Results

```
/home/luispedro/.mambaforge/envs/py3.11/lib/python3.11/site-packages/sklearn/neighbors/_base.py:248: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
  warnings.warn(
/home/luispedro/.mambaforge/envs/py3.11/lib/python3.11/site-packages/sklearn/neighbors/_base.py:248: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
  warnings.warn(
```

### Versions

```shell
I tested on the current main branch, 5cdbbf15e3fade7cc2462ef66dc4ea0f37f390e3, but it has been going on for a while (see original SemiBin report from September 2024):


System:
    python: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0]
executable: /home/luispedro/.mambaforge/envs/py3.11/bin/python3.11
   machine: Linux-6.8.0-55-generic-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.7.dev0
          pip: 24.0
   setuptools: 70.0.0
        numpy: 1.26.4
        scipy: 1.13.1
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.8.4
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 16
         prefix: libopenblas
       filepath: /home/luispedro/.mambaforge/envs/py3.11/lib/libopenblasp-r0.3.27.so
        version: 0.3.27
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 16
         prefix: libgomp
       filepath: /home/luispedro/.mambaforge/envs/py3.11/lib/libgomp.so.1.0.0
        version: None
```

COMMENT:
Apologies, I wasn't aware of this protocol. I had reproduced the error yesterday, and tracked the problem down to the same line. I am not entirely sure why this issue is occurring - I assume it is something to do with how sparse matrices are stored - and when we concretely store the diagonal, this changes the ordering of the data. I was intending to solve it by looking into how the scipy csr matrices are stored, the calculations inside _is_sorted_by_data, and what changes before and after X.setdiag(X.diagonal()).

Labels: Bug, help wanted, Needs Investigation