feat: add vector distance and array math functions

[crm26]

Summary

Adds vector distance and array math functions to datafusion-functions-nested, enabling vector search and array algebra in standard SQL.

-- Vector search: find nearest neighbors by cosine distance
SELECT id, cosine_distance(embedding, ARRAY[0.1, 0.2, ...]) as dist
FROM documents ORDER BY dist LIMIT 10

-- Array math
SELECT array_normalize(embedding) FROM documents
SELECT array_add(vec_a, vec_b) FROM t
SELECT array_scale(embedding, 2.0) FROM documents

Functions

Function	Returns	Description
cosine_distance(a, b)	float64	1 - cosine similarity
inner_product(a, b)	float64	Dot product
array_normalize(a)	list(float64)	Unit vector
array_add(a, b)	list(float64)	Element-wise addition
array_subtract(a, b)	list(float64)	Element-wise subtraction
array_scale(a, f)	list(float64)	Scalar multiplication

All have list_* aliases. inner_product also aliased as dot_product.

Design

Shared primitives in vector_math.rs:

• dot_product_f64(a, b) — used by inner_product and cosine_distance
• magnitude_f64(a) — used by cosine_distance and array_normalize
• sum_of_squares_f64(a) — used by magnitude_f64
• convert_to_f64_array(a) — shared with existing array_distance

The existing distance.rs duplicate convert_to_f64_array is consolidated into the shared module.

Follows the exact pattern of the existing array_distance function: same signature style, coerce_types, null handling, and type support (Float32, Float64, Int32, Int64, FixedSizeList, LargeList, List).

Tests

79 tests including: normal inputs, null handling, zero vectors, orthogonal vectors, empty arrays, Float32/Float64, mismatched lengths, vector search ranking pattern. Sqllogictest coverage in vector_functions.slt. Clippy clean.