Empty struct columns silently fall back to Spark instead of running natively

[unikdahal]

Describe the bug

Six independent copies of the same guard reject a StructType with zero fields
(fields.nonEmpty && fields.forall(...)), collapsing "empty struct" into the same
false as a genuinely unsupported type. An empty struct is a legitimate,
trivially-serializable Arrow value -- zero child arrays, its own validity bitmap --
there's no technical reason to exclude it from the five Scala-side type-support checks
below. The sixth is different: it guards a real native panic, not just a planning-time
rejection (see below).

Found via Apache Iceberg's _partition metadata column, which is exactly StructType()
on an unpartitioned table. Any plan carrying that column (e.g. _partition selected
alongside _file/_pos for row-level MERGE/UPDATE/DELETE tracking) silently fell back
to Spark the moment it hit a shuffle, sink, or local-table-scan boundary -- no error,
just a quiet loss of native execution for everything downstream.

• CometShuffleExchangeExec.scala -- native shuffle's type check
• CometShuffleExchangeExec.scala -- columnar shuffle's type check (separate function,
  same file)
• CometSink.scala -- the sink operator reading off a shuffle/exchange boundary, also
  the gate for LocalTableScanExec
• QueryPlanSerde.scala -- the core expression-level type gate (hit via a
  Literal(_, StructType()) placeholder)
• DataTypeSupport.scala -- the general operator schema gate
• serde/structs.scala -- from_json's target-schema check. Fixing this one alone
  surfaced a real native panic: native/spark-expr/src/json_funcs/from_json.rs builds
  the result via StructArray::new(fields, arrays, nulls), which derives row count from
  the first child array and panics when fields is empty (no child array to derive it
  from). Fixed by branching to StructArray::new_empty_fields(len, nulls) when
  fields.is_empty().

Steps to reproduce

val schema = StructType(
  Seq(StructField("id", IntegerType), StructField("marker", StructType(Nil))))
val data = (0 until 50).map(i => Row(i, Row()))
val df = spark.createDataFrame(data.asJava, schema)
df.repartition(10, $"id").collect()

With spark.comet.exec.shuffle.enabled=true, the shuffle silently ran as plain Spark
ShuffleExchangeExec, not CometShuffleExchangeExec -- no fallback reason surfaced
unless spark.comet.explain.fallback.log.enabled=true was set, which then logged:
unsupported shuffle data type StructType() for input <col>.

Separately, with the native from_json path opted in
(spark.comet.expression.JsonToStructs.allowIncompatible=true),
from_json(col, 'struct<>') crashed the executor with:
CometNativeException: native panic: called Result::unwrap() on an Err value: InvalidArgumentError("use StructArray::try_new_with_length or StructArray::new_empty_fields...").

Expected behavior

An empty-struct column should not, by itself, prevent native execution -- Comet should
run natively the same way it does for a non-empty struct, and never panic.

Additional context

Discovered while getting native MergeRowsExec (Comet's row-level MERGE dispatch
operator) to engage for Iceberg merge-on-read tables: scan, join, and merge dispatch
all converted to native operators, but the chain silently broke the moment _partition
entered a shuffle boundary. Fix + tests (general-purpose, not Iceberg-specific) ready on
branch fix-empty-struct-shuffle-support, independent of any Iceberg-specific work.