Chunked `jacobian` throws for `Diagonal`/`LowerTriangular`/`UpperTriangular` inputs

jacobian allocates its result with chunksize(cfg) columns, which is structural_length(x), but reshape_jacobian reshapes it to length(xdual) columns. For inputs whose structural length differs from their length the two disagree, so chunk mode always throws while vector mode works.

julia> using ForwardDiff, LinearAlgebra

julia> g(z) = [sum(z), sum(abs2, z)];

julia> x = UpperTriangular(rand(3, 3));

julia> size(ForwardDiff.jacobian(g, x))   # vector mode
(2, 6)

julia> ForwardDiff.jacobian(g, x, ForwardDiff.JacobianConfig(g, x, ForwardDiff.Chunk{2}()))
ERROR: DimensionMismatch: new dimensions (2, 9) must be consistent with array length 12

All three wrapper types, only the full-length chunk survives:

Since Chunk(x) picks the full structural length for anything up to DEFAULT_CHUNK_THRESHOLD, this only shows up for larger inputs or an explicit Chunk. Introduced in #739.

Worth deciding the column convention at the same time: jacobian currently uses structural columns while gradient returns a result shaped like x. See the discussion in #837, where I argued for indexing the Hessian by the linear indices of x on both axes.

ForwardDiff v1.4.5, Julia 1.12.6.

Begin with the chunked jacobian path, chunksize(cfg), and reshape_jacobian, then review the column-convention discussion in #837. Verify completion using the shown UpperTriangular, LowerTriangular, and Diagonal examples with chunk sizes 1, 2, and structural length, comparing the results with vector mode.