bench: apply mcode arena hardening across all bench scripts (3qu)

Added jit.opt.start('sizemcode=64', 'maxmcode=4096') to all 9 bench
scripts. Same fix 3o2 landed in bench/jit_trace.lua, propagated to the
rest. Without it, the macOS arm64 mcode allocator intermittently fails
to find an executable page in the signed-32-bit offset window and the
bench reports interpreter-only throughput with no diagnostic — visible
as silent regressions. The risk is higher in scripts with larger codegen
footprints than the trace gate.

Files touched: bench.lua, lazy_bench.lua, profile.lua, shapes_bench.lua,
starwing_bench.lua, wire_bench.lua, alloc_probe.lua, map_bench.lua,
packed_bench.lua. All 7 non-interactive scripts run rc=0; profile and
starwing loadfile-check clean.

Also refreshed bench/baseline.json per the issue's step 2. Surprise win:
full-mode decode allocations dropped 0.5-50% as a side-effect of auj/ozn
that was only visible after the snapshot. Most notable: proto2_basic
BenchPayload mid decode 2.313 -> 1.156 KB/op (-50%), Person 1KB decode
0.977 -> 0.953 KB/op. Runtime-mode unchanged — confirms the alloc
reduction is full-mode codegen specific.

Steps 3-4 (refresh COMPARISON.md throughput tables, verify the variance
band shrinks below the documented 5-10% drift) are deferred to a
work.lab.local run — laptop variance is 50%+ on identical state, can't
trust throughput A/Bs locally.

bench: jit.p profile driver for hot encode/decode paths

Adds bench/profile.lua, a one-shot driver around LuaJIT's
sampling profiler (jit.p). Runs Person_encode and Person_decode
against the 1 KB fixture — the size where decode MB/s halves and
encode MB/s stops climbing — and prints a function-and-line
breakdown of where wall time actually goes.

Used during the M5 perf-investigation arc to produce the
attributions captured in the jit-p-profile-2026-05-18 memo and
referenced by several bd issues (h8v, u39, gcy, 4kj, aah, bgu).

`tarantool bench/profile.lua` runs both passes; `encode` /
`decode` arguments limit to one phase. Sample rate is 4 ms and
the loop runs 200 000 iterations so each phase produces ~500
samples — enough to attribute single-percent line cost.