Ranked backlog: native scalar expressions with per-row optimization opportunities

Follow-up to #4936. A scan of the native scalar expression implementations in `datafusion-comet-spark-expr` (`native/spark-expr/`) for per-row antipatterns: per-element builder loops that could be vectorized Arrow kernels, `iter().map().collect()` over Option/Result, per-row heap allocation (`String`/`format!`/`to_string`), per-row `value(i)` allocation, per-element `MutableArrayData::extend`, and constants recomputed inside the per-row loop.

Each is a small self-contained change. Methodology (benchmark-first, bit-identical, no-regression gate): see the [Optimizing Scalar Expressions](https://github.com/apache/datafusion-comet/blob/main/docs/source/contributor-guide/optimizing_expressions.md) guide (PR #4933). Ranked by potential impact = per-row cost severity x usage frequency.

## High impact (decimal aggregation hot path, common in TPC-DS)

- [ ] **`spark_unscaled_value`** (`math_funcs/internal/unscaled_value.rs:37`) - per-element `Int64Builder` loop casting Decimal128 -> Int64, rebuilding an identical null buffer. Fix: `arr.unary::<_, Int64Type>(|v| v as i64)`. Injected by Spark's `DecimalAggregates` rule for `sum`/`avg` over narrow decimals.
- [ ] **`spark_make_decimal`** (`math_funcs/internal/make_decimal.rs:45`) - per-element `Decimal128Builder` loop, Int64 -> Decimal128. Fix: `unary_opt` (`long_to_decimal` already returns `Option`). Paired with `UnscaledValue` on the output side of the same rewrite.

## Medium impact

- [ ] **`list_extract`** (`array_funcs/list_extract.rs:285`) - per-row `MutableArrayData::extend(0, start+i, start+i+1)` (one element/row) plus a `Box` index adjuster called per row. Fix: build a gather-index `UInt32Array` and issue one `arrow::compute::take`; monomorphize the index adjustment. Backs `GetArrayItem` (`arr[i]`) and `element_at`.
- [ ] **`cast_int_to_timestamp`** (`conversion_funcs/numeric.rs:1006`) - per-element `TimestampMicrosecondBuilder` loop over an infallible `(v as i64) * MICROS_PER_SECOND`. Fix: `unary` + `with_timezone_opt`.
- [ ] **`read_side_padding` (rpad/CHAR(n))** (`static_invoke/char_varchar_utils/read_side_padding.rs:213,275`) - per-row `chars().count()` full scan (twice on the truncate branch) and char-by-char `push` for padding. Fix: scan length once; bulk-fill the pad run (`extend`/`push_str` of a cached pad slice). Runs on CHAR(n) columns.
- [ ] **`from_json` / `json_string_to_struct`** (`json_funcs/from_json.rs:128`) - allocates a full `serde_json::Value` tree per row just to read named fields, plus `value.to_string()` for non-string fields. Fix: arrow-json's streaming decoder (`arrow_json::ReaderBuilder`) or a borrowing visitor. Common in JSON ETL (not TPC-DS).
- [ ] **`NegativeExpr` ANSI overflow check** (`math_funcs/negative.rs:62`) - an extra full per-row scan for `MIN` before the (already vectorized) negation, in ANSI mode. Fix: `eq_scalar(array, MIN)` + `any` in one pass.
- [ ] **`spark_cast_decimal_to_boolean`** (`conversion_funcs/numeric.rs:790`) - per-element `BooleanBuilder` loop (`!v.is_zero()`). Fix: `neq` against a zero Decimal128 scalar, or single-pass `iter().map().collect::()`.
- [ ] **`cast_float_to_timestamp`** (`conversion_funcs/numeric.rs:1056`) - per-element builder loop with NaN/inf/overflow checks. Fix: `try_unary` (ANSI) / `unary_opt` (Legacy/Try).
- [ ] **`cast_decimal_to_timestamp`** (`conversion_funcs/numeric.rs:1030`) - per-element builder loop; `scale_factor` already hoisted. Fix: `unary` (transform is infallible).

## Low impact

- [ ] **`cast_boolean_to_timestamp`** (`conversion_funcs/boolean.rs:60`) - per-element builder loop (`bool -> 0/1`). Fix: single-pass `iter().map().collect()`.
- [ ] **`cast_whole_num_to_binary`** (`conversion_funcs/numeric.rs:213`) - per-element `BinaryBuilder` loop; binary building is inherently element-wise, but the null branch can be dropped when `null_count() == 0`.
- [ ] **`date_trunc` / `timestamp_trunc` array-format variants** (`kernels/temporal.rs:435,783`) - per-row `format.value(i).to_uppercase()` allocation + per-row format->fn string match. Fix: `eq_ignore_ascii_case` (no allocation); resolve the trunc fn once per distinct format key. Only the array-of-formats path; the literal-format scalar path is already fast.
- [ ] **`array_position` nested-type fallback** (`array_funcs/array_position.rs:264`) - per-element `ScalarValue::try_from_array` allocation. Only triggered for nested/struct element types (primitives/strings/bools already have flat fast paths).
- [ ] **`json_array_length`** (`json_funcs/json_array_length.rs:114`) - near-optimal (streaming counter); minor once-per-scalar `clone`.

## Notes

- Already optimized or in flight (excluded from the list): the decimal/int/float casts and narrowing int casts (PRs #4934, #4937-#4941), plus the earlier campaign (`spark_cast_int_to_int`, `to_json`, `parse_url`, `spark_size`, `spark_unhex`, `substring`, `date_trunc` scalar path, `spark_arrays_overlap`, `arrays_zip`, `get_json_object`, `regexp_extract`/`_all`, `spark_lpad`, casts to string, etc.).
- Aggregate functions (`agg_funcs/`) were out of scope for this scan.
- Confirmed NOT candidates (already vectorized or inherently scalar): `contains`, `split`, `map_sort`, `flatten`, `get_array_struct_fields`, `normalize_nan`, `spark_decimal_div`, `spark_modulo`, `WideDecimalBinaryExpr`, murmur3/xxhash64 hashing, string-parse casts (`utf8 -> int/float/decimal/timestamp`), `make_date`/`make_time`, and `rlike`/`rand` (no Arrow kernel applies). All `Regex::new` calls are already hoisted into `LazyLock` statics.