gh-40493: Euclidean norm crashfix sparse matrices
    
<!-- ^ Please provide a concise and informative title. -->
<!-- ^ Don't put issue numbers in the title, do this in the PR
description below. -->
<!-- ^ For example, instead of "Fixes #12345" use "Introduce new method
to calculate 1 + 2". -->
<!-- v Describe your changes below in detail. -->
<!-- v Why is this change required? What problem does it solve? -->
<!-- v If this PR resolves an open issue, please link to it here. For
example, "Fixes #12345". -->
Fixes #40492.

The call to `.norm()` or `norm()` with default parameter (`p = 2` for
euclidean norm) crashes for sparse matrices.
I go around it by checking if the matrix is sparse and if so, copy it
into a dense matrix internally to compute the norm. This must be done
since the euclidean norm uses the `.SVD()` method which is not exposed
for sparse matrices.




### 📝 Checklist

<!-- Put an `x` in all the boxes that apply. -->

- [x] The title is concise and informative.
- [x] The description explains in detail what this PR is about.
- [x] I have linked a relevant issue or discussion.
- [x] I have created tests covering the changes.
- [ ] I have updated the documentation and checked the documentation
preview.

### ⌛ Dependencies

<!-- List all open PRs that this PR logically depends on. For example,
-->
<!-- - #12345: short description why this is a dependency -->
<!-- - #34567: ... -->
    
URL: #40493
Reported by: grnx
Reviewer(s): grnx, user202729

Author: Release Manager

src/sage/matrix/matrix2.pyx

@@ -16484,6 +16484,11 @@ cdef class Matrix(Matrix1):
 
             sage: matrix(CDF, 2, 2, sparse=True).norm(1)
             0.0
+
+        Check the euclidean norm for a sparse matrix (:issue:`40492`)::
+
+            sage: matrix(ZZ, [[1, 2], [3, 4]], sparse=True).norm()
+            5.464985704219043
         """
         from sage.rings.real_double import RDF
 
@@ -16494,8 +16499,11 @@ cdef class Matrix(Matrix1):
         if p == 2:
             from sage.rings.complex_double import CDF
 
-            A = self.change_ring(CDF)
-            A = A.conjugate().transpose() * A
+            # Always try to convert to ``dense_matrix`` since sparse matrices
+            # don't expose the ``SVD`` method. If the matrix is already dense,
+            # the cost is negligible.
+            A = self.dense_matrix().change_ring(CDF)
+            A = A.conjugate_transpose() * A
             S = A.SVD()[1]
             return max(S.list()).real().sqrt()