DataModel: validate constraint array lengths against the row count #2254

Problem

DataModel accepts two row representations: rhs + row types (set_constraint_bounds, set_row_types) and lower/upper bounds (set_constraint_lower_bounds, set_constraint_upper_bounds). The setters in data_model_wrapper.pyx store whatever array they receive. Nothing checks that its length equals the row count implied by A_offsets (len(A_offsets) - 1).

The solve path then passes each array to the C++ data_model_view with its own shape[0]. Any consumer that assumes one representation, or assumes the sizes match, can read out of bounds. #2249 is one instance, in _compute_slack_csr, which indexed rhs[i] and sense[i] when only the bounds were set. cvxpy, pyomo and linopy all use set_constraint_lower_bounds, so this path is exercised by third-party interfaces.

Proposal

Validate at the Python boundary (the setters, or the solve entry once the matrix is known):

• rhs, row_types, constraint_lower_bounds and constraint_upper_bounds must each be empty or have length equal to the row count.
• Lower and upper bounds must be set together or not at all.
• Raise a clear ValueError otherwise.

Optionally, as Ishika Roy suggested, normalize so that rhs + sense is always converted to lower/upper bounds when the model is built, so consumers only deal with one representation. That is a larger change and could be a second step.

Open question

Does the C++ data_model_view already validate these sizes against the row count? That needs checking, since the Python-side check would then be a duplicate of it.

Related: #2249, #2253, #1615.