refactor: use schema extensions for protocol-aware JSON/CBOR naming

[jterapin]

Context

This is the second PR in the stack and should be reviewed on top of #351.

PR #351 moved JSON and CBOR structure serde onto the actual input or payload data, but the follow-up lookup path was still protocol-blind and relied on temporary normalization. This PR moves that lookup behavior behind explicit schema and JSON extension helpers.

What changed

• Added Smithy::Schema::Extension for generic modeled-member lookup and shared trait helpers.
• Added Smithy::Json::Extension for jsonName-aware lookup used only when JSON serde opts into json_name behavior.
• Cached generic member lookup indexes on shapes as shape[:member_index].
• Cached JSON member lookup indexes on shapes as shape[:json_index].
• Cached resolved JSON wire names on members as member[:json_name].
• Restored MemberShape#name as the modeled member name and updated generated schema emission accordingly.
• Kept raw Smithy trait data string-keyed rather than symbol-normalizing it so future dynamic-client paths can continue resolving traits directly from model-shaped data.
• Routed document serde jsonName handling through the same extension helpers.
• Moved generic sparse? handling to Smithy::Schema::Extension, with JSON delegating to it.

Behavior after this change

• JSON with json_name: true uses jsonName.
• JSON with json_name: false uses the modeled member name.
• CBOR uses the modeled member name.
• Union __type is only preserved when it is explicitly modeled through jsonName.

Explicit non-goals

• No XML behavior changes in this PR.
• No Query behavior changes in this PR.

Compatibility note

MemberShape#initialize still accepts location_name: as a temporary fallback for downstream stacked work and existing generated projections.

Validation

• bundle exec rspec gems/smithy-schema/spec gems/smithy-json/spec gems/smithy/spec/interfaces/schema/serde_traits_spec.rb
• bundle exec smithy build --debug

Performance Summary

JSON Wins And Regressions

Top JSON Wins

• Large AWS JSON GetItem response with binary-heavy payload: 16.4% lower mean latency on Graviton, 10.4% lower on Intel
• Baseline AWS JSON PutItem request: 14.0% lower mean latency on Intel, 13.4% lower on Graviton
• Small AWS JSON PutItem request with binary payload: 13.4% lower mean latency on Intel, 10.2% lower on Graviton
• Baseline AWS JSON GetItem response: 12.7% lower mean latency on Intel, 7.2% lower on Graviton
• Large AWS JSON GetItem response: 11.3% lower mean latency on Graviton, 8.2% lower on Intel

Notes:

• The clearest wins are concentrated in AWS JSON GetItem / PutItem scenarios, especially baseline and binary-heavy payloads.
• Intel shows the broader set of >=5% wins, while Graviton still improves on the largest JSON cases.
• Overall, JSON is the strongest-performing protocol family in this benchmark set.

Top JSON Regressions

• AWS JSON healthcheck example response: 15.4% higher mean latency on Intel, 10.4% higher on Graviton
• AWS JSON healthcheck example request: 7.4% higher mean latency on Graviton
• Small AWS JSON GetItem response payload: 4.6% higher mean latency on Intel

Notes:

• The healthcheck scenarios are the only clear JSON regressions above the +5% threshold.
• Outside those outliers, the remaining JSON regressions are low single-digit and read as near-baseline rather than meaningful movement.

CBOR Wins And Regressions

Top CBOR Wins

• Small CBOR PutItem request with binary payload: 9.2% lower mean latency on Intel
• Baseline CBOR PutItem request: 8.4% lower mean latency on Intel, 6.7% lower on Graviton
• Baseline CBOR GetItem response: 6.0% lower mean latency on Intel, 5.5% lower on Graviton
• Medium nested CBOR PutItem request: 5.4% lower mean latency on Intel

Notes:

• CBOR shows a handful of real wins, mostly on baseline and PutItem request shapes, but the overall effect is modest rather than dramatic.
• Across all CBOR serde cases, mean latency is 1.4% lower and p99 latency is 2.2% lower, so most results still cluster close to baseline.

Top CBOR Regressions

• No CBOR scenario crosses a +5% regression threshold.

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Written with AI assistance and reviewed by jterapin.