Add get_columns_by_pattern macro

[FeatherAnalytics]

resolves #1037

Problem

Users working with datasets that have dynamically named columns (like year-based columns: 2024_revenue, 2023_revenue, or similarly named fields: current_item, last_item) need a way to programmatically select columns matching a pattern. While this can be done with a for loop in Jinja, there's no idiomatic dbt-utils macro for this common use case.

Solution

This PR adds a new get_columns_by_pattern macro that returns columns from a relation matching a SQL LIKE pattern.

Features:

• Supports SQL LIKE pattern syntax (% for wildcard, _ for single character)
• Optional exclude parameter to filter out unwanted columns
• Case-insensitive matching (consistent with SQL standards)
• Returns a simple list of column names for easy composition with other macros

Implementation follows existing dbt-utils patterns:

• Uses adapter.dispatch() for cross-database compatibility
• Includes input validation with _is_relation and _is_ephemeral
• Guards against parsing mode with if not execute
• Mirrors the design of get_filtered_columns_in_relation

Example usage:

-- Get all columns ending with '_item'
{% set item_columns = dbt_utils.get_columns_by_pattern(
    from=ref('sales'),
    pattern='%_item'
) %}

-- Get year columns excluding archived ones  
{% set year_columns = dbt_utils.get_columns_by_pattern(
    from=ref('revenue'),
    pattern='20%',
    exclude='%_archived'
) %}

Checklist

• This code is associated with an issue which has been triaged and accepted for development
• I have read the contributing guide and understand what's expected of me
• I have run this code in development and it appears to resolve the stated issue
• This PR includes tests (integration test with seed data and equality assertion)
• I have updated the README.md (added documentation under "Introspective macros" section)