JSON_EXTRACT: zero-copy byte slicing for object, array, and number extraction

[quackaplop]

Summary

Optimizes JSON_EXTRACT to use zero-copy byte slicing instead of copyCurrentStructure()
re-serialization when extracting objects, arrays, and numbers from JSON input. This builds on
the byte offset API exposed in #143501.

What changed

Object/array extraction — Previously, extracting a nested object or array walked every token
in the subtree and rebuilt JSON from scratch via XContentBuilder.copyCurrentStructure(). Now it
slices bytes directly from the input buffer using getTokenLocation().byteOffset() →
skipChildren() → getCurrentLocation().byteOffset(). Zero allocation, zero re-parsing.

Number extraction — Previously called parser.text() which makes Jackson convert the number
to a Java String, then wraps in BytesRef. Now byte-slices the number literal directly from
the input array, avoiding the String allocation entirely.

Boolean extraction — Reuses static TRUE_BYTES / FALSE_BYTES constants instead of
allocating a new BytesRef("true") / BytesRef("false") per call.

Navigation refactoring — Replaced recursive descent (extractValue → navigateObject →
extractValue → ...) with an iterative loop. Navigation methods are now pure parser-positioning
helpers that don't need the byte-slicing context, keeping raw byte access confined to the
extraction point.

Non-JSON _source formats (SMILE/CBOR/YAML) fall back to copyCurrentStructure().

Benchmarks

Also adds json_extract and json_extract_object scenarios to EvalBenchmark, and a
dedicated JsonExtractBenchmark with 10 scenarios through the full eval pipeline (EvalMapper →
Layout → Page → Evaluator).

Environment: Apple M3 Max, JDK 25.0.1, JMH 1.37, warmup 3×2s, measurement 5×2s.

Scenario	Before (ns/op)	After (ns/op)	Change
small_object (30B)	222.0 ± 2.8	115.9 ± 3.1	-47.8%
medium_object (500B)	1,275.9 ± 27.0	662.2 ± 15.7	-48.1%
large_object (4KB)	24,531.3 ± 1,641	15,938.0 ± 721	-35.0%
large_nested_extract (10KB doc)	12,323.1 ± 458	6,664.0 ± 180	-45.9%
array_of_objects ([25] of 50)	4,253.0 ± 76	3,853.5 ± 68	-9.4%
nested_scalar (5 levels)	206.2 ± 4.9	178.9 ± 3.1	-13.2%
deep_nesting (10 levels)	478.7 ± 54	324.9 ± 10.7	-32.1%
number	160.0 ± 2.4	133.1 ± 3.2	-16.8%
boolean	106.0 ± 2.1	100.6 ± 2.5	-5.1%
string	107.6 ± 2.8	103.1 ± 2.0	-4.2%

Largest wins on object/array extraction (35–48%) where copyCurrentStructure was the hot path.

Relates #142873

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