Native CartesianProductExec: multi-partition inputs and spilling #7476

#6938 runs CartesianProductExec natively behind spark.comet.exec.cartesianProduct.enabled (default false), only without a join condition and when each input reports exactly one partition. Remaining work:

• Multi-partition inputs. Spark crosses every partition pair through CartesianRDD. A non-bucketed Parquet scan reports UnknownPartitioning(0), so today the native path needs a COALESCE(1) or similar.
• Spilling. CrossJoinExec buffers the whole left input and cannot spill (CrossJoinExec buffers the entire left input without spilling (OOM under memory pressure) datafusion#26383). Its concat_batches copy of the left side is also unreserved.
• Large string and binary columns. That concatenation is exposed to the 2 GiB offset limit (Offset overflow - Hash join and nested loop join build side concatenated into one batch (upstream) #7273).
• Output batching. One output batch per left row for each right batch, which explains the 3.1× large-left benchmark result against 18.6× for the square case.
• Row order. Native output is right-batch-major, Spark's is left-row-major. Documented, since Spark reports no output ordering.