feat(datafusion): fill omitted INSERT columns with Iceberg write-default values

[moomindani]

Which issue does this PR close?

• Closes DataFusion INSERT fills omitted columns with NULL instead of the column's write-default #2803. Part of EPIC: v3 Support Tracking #2411 (Default Values).

What changes are included in this PR?

Per the spec, writers must use a column's write-default for columns they do not supply. DataFusion's insert planner consults TableProvider::get_column_default for columns omitted from an INSERT and falls back to NULL; IcebergTableProvider did not implement it, so tables with write-default values silently got NULLs.

• IcebergTableProvider caches the schema's top-level write-default values as DataFusion expressions at construction and serves them via get_column_default.
• Adds a literal_to_scalar_value conversion covering the primitive types (boolean, int, long, float, double, string, date, time, timestamp/timestamptz in µs and ns, decimal, binary, fixed, uuid); defaults with no scalar representation are skipped. The planner casts the expression to the target arrow type, so representation differences are reconciled downstream.
• This mirrors iceberg-java's approach of delegating write-default application to the engine: its Spark integration exposes write-default as the engine column default (TypeToSparkType), and Spark materializes it at INSERT planning.
• The static provider is read-only and keeps the trait default (no column defaults).

Are these changes tested?

Yes — an end-to-end test (INSERT omitting defaulted columns, then scanning to assert the defaults land and explicitly provided values win) plus unit tests for the literal conversion. The end-to-end test fails without the get_column_default implementation. cargo test -p iceberg-datafusion --lib (90 tests) and cargo clippy -p iceberg-datafusion --lib --tests pass locally.

This pull request and its description were written by Claude Fable 5.