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:
Hi @adrinjalali and @yuwei-1, I have also looked into this issue. 
I will break it down and the possible next steps.

Firstly, we do need the `X.setdiag(X.diagonal())` line because csr arrays only store non-zero elements explicitly. If diagonal elements are missing (which is common in distance matrices), DBSCAN adds them but also changes the internal structure in the process.
There are documented issues with `DBSCAN` when diagonal elements are missing
[#8306](https://github.com/scikit-learn/scikit-learn/issues/8306) and [#439](https://github.com/scikit-learn-contrib/hdbscan/issues/439).

So this means we must sort during or after the internal structure of the matrix is changed. 
This is the most efficient way to fix the warning. It has to be an internal change.

### Proposed Solution ( I agree with @Luis-Varona )
```python
      X.setdiag(X.diagonal())
      # 2. Re‑sort rows so they are monotone by value again
      from sklearn.neighbors import sort_graph_by_row_values
      sort_graph_by_row_values(X, copy=False, warn_when_not_sorted=False) #<--- This sorts the matrix only if it is not sorted
```
### Observe the structure of the matrix at each point.
```python
from scipy.sparse import csr_array
from sklearn.neighbors._base import _is_sorted_by_data
from sklearn.neighbors import sort_graph_by_row_values

A = csr_array([[0,2],[3,0]], dtype=float)
print(A)
print(_is_sorted_by_data(A))        # True

A.setdiag(A.diagonal())             # materialise zeros
print(A)
print(_is_sorted_by_data(A))        # False

# sort data
sort_graph_by_row_values(A, copy=False, warn_when_not_sorted=False)
print(A)
print(_is_sorted_by_data(A))        # True
```

#### Output
```
<Compressed Sparse Row sparse array of dtype 'float64'
        with 2 stored elements and shape (2, 2)>
  Coords        Values
  (0, 1)        2.0
  (1, 0)        3.0
True
<Compressed Sparse Row sparse array of dtype 'float64'
        with 4 stored elements and shape (2, 2)>
  Coords        Values
  (0, 1)        2.0
  (0, 0)        0.0
  (1, 0)        3.0
  (1, 1)        0.0
False
<Compressed Sparse Row sparse array of dtype 'float64'
        with 4 stored elements and shape (2, 2)>
  Coords        Values
  (0, 0)        0.0
  (0, 1)        2.0
  (1, 1)        0.0
  (1, 0)        3.0
True
```

Labels: Bug, help wanted, Needs Investigation