…okup table (#1334)

* Fix _qderiv_actuator_passive_actuation_sparse row indexing into qM_fullm

The kernel introduced in #1243 searches qM_fullm for the (row, col) elemid
using m.M_rownnz / m.M_rowadr. Those arrays describe MuJoCo's compact mass
matrix sparsity, where joints whose internal block is treated as
diagonal-only by mj_factorM (e.g. free joints) contribute one entry per
internal dof. qM_fullm_i / qM_fullm_j, however, are built by walking
dof_parentid and include the full chained internal block, so the two
layouts diverge whenever a joint with diagonal-only compact storage
precedes any actuated dof in qvel order.

In that case the kernel reads the wrong slice of qMj, the inner
`qMj[row_startk] == col` check never fires, and the actuator's
contribution to qDeriv is silently dropped. Downstream factor_solve_i
then sees (M - dt*qDeriv) without the actuator damping for the affected
dof, and the implicit step diverges. The bug only surfaces with this
specific topology (free joint at qvel start + actuated dof after it),
which is why the existing serial-chain unit test (PR #1243's
test_smooth_vel_sparse_tendon_coupled, no free joint) does not catch it.

Repro: a single free body followed by an actuated hinge; with the
buggy indexing, mjwarp's deriv_smooth_vel diagonal entry for the hinge
is qM only (0.001), while MuJoCo's reference is qM + dt*(kp+kv) (0.003).

Fix: build chain-aware row offsets qM_fullm_rownnz / qM_fullm_rowadr
alongside qM_fullm_i / qM_fullm_j in io.py and pass them to the kernel
instead of the compact M_rownnz / M_rowadr.

Adds test_smooth_vel_sparse_free_joint_precedes_actuator covering the
minimum reproducer.

* Address review: drop unused body name and is_sparse assert

Per @thowell's PR feedback (#1334), remove the redundant body name
attribute and the is_sparse assertion in the new regression test.

* Switch sparse qDeriv lookup to qM_fullm_elemid (Kenny's approach)

Replace the chain-aware (qM_fullm_rownnz, qM_fullm_rowadr) row offsets
plus linear search through qMj with a dense (nv x nv) qM_fullm_elemid
lookup table built in io.py. The kernel does an O(1) reverse lookup
instead of walking the row to find the matching column.

For typical robot models (humanoid, three_humanoids) kernel time is
unchanged within trial noise. For deep chains with high-rownnz actuators
(e.g. tendons spanning a long serial chain) the kernel asymptotically
drops from O(depth^3) to O(depth^2) per actuator thread; a 100-link
chain with a tendon actuator runs ~8x faster end-to-end on a 5090.

Memory cost: nv^2 * 4 bytes (40 KiB at nv=100, 1 MiB at nv=500).