DOC: update np.shares_memory() docs (numpy#27090)

Description

In NumPy 2.0, the MAY_SHARE_EXACT and MAY_SHARE_BOUNDS constants are no longer exposed as part of the public API. This PR updates the documentation for np.shares_memory functions to reflect this change.

Changes made

Replaced MAY_SHARE_EXACT with its actual value -1 in the documentation.
Replaced MAY_SHARE_BOUNDS with its actual value 0 in the documentation.
Updated the warning message to remove references to MAY_SHARE_BOUNDS.
These changes ensure that the documentation accurately reflects the current implementation and provides clear guidance for users on how to correctly use these functions in NumPy 2.0.

Close numpy#27089

Co-authored-by: Sebastian Berg <[email protected]>

Author: guan404ming

numpy/_core/multiarray.py

@@ -1350,7 +1350,7 @@ def shares_memory(a, b, max_work=None):
     .. warning::
 
        This function can be exponentially slow for some inputs, unless
-       `max_work` is set to a finite number or ``MAY_SHARE_BOUNDS``.
+       `max_work` is set to zero or a positive integer.
        If in doubt, use `numpy.may_share_memory` instead.
 
     Parameters
@@ -1362,12 +1362,13 @@ def shares_memory(a, b, max_work=None):
         of candidate solutions to consider). The following special
         values are recognized:
 
-        max_work=MAY_SHARE_EXACT  (default)
+        max_work=-1 (default)
             The problem is solved exactly. In this case, the function returns
             True only if there is an element shared between the arrays. Finding
             the exact solution may take extremely long in some cases.
-        max_work=MAY_SHARE_BOUNDS
+        max_work=0
             Only the memory bounds of a and b are checked.
+            This is equivalent to using ``may_share_memory()``.
 
     Raises
     ------