fix: override is_string/is_number/is_float type classification for Databricks types

[psaikaushik]

Summary

col.is_string(), col.is_number(), col.is_float(), and col.is_numeric() all return False for Databricks/Spark column types because the base Column class only recognizes PostgreSQL-style type names.

Closes #1380
Related: dbt-labs/dbt-spark#658

Root Cause

The base Column class from dbt-adapters defines is_string() etc. using type name sets that include PostgreSQL types (text, character varying, etc.) but not Spark/Databricks types (string, int, bigint, double, etc.).

DatabricksColumn inherits from SparkColumn which inherits from the base Column, but neither overrides these classification methods.

Fix

Override is_string(), is_number(), is_float(), is_integer(), and is_numeric() on DatabricksColumn with the complete set of Spark/Databricks type names:

• String types: string, varchar, char, text, etc.
• Numeric types: tinyint, smallint, int, integer, bigint, long, float, double, decimal, numeric, real (plus decimal(p,s) variants)
• Float types: float, double, real
• Integer types: tinyint, smallint, int, integer, bigint, long

Verification

Column: name03 | Data Type: string | Is String: True  (was: False)
Column: age    | Data Type: int    | Is Number: True  (was: False)

Checklist

• No breaking changes — only adds overrides, doesn't change any existing behavior for correctly-typed columns
• Covers all Spark/Databricks primitive types