Prepare Utils codebase for Spark ( Iceberg ) Support

[ilias1111]

Description

1. Added a variable for the file format configuration, defaulting to delta with iceberg as an alternative for Spark targets.
2. Modified dbt_project.yml to include the file format configuration for models based on the target type.
3. Implemented custom date_diff and date_add functions in timestamp_functions.sql, casting results as bigint to prevent overflow.
4. Enhanced the get_value_by_target_type macro to include spark as a valid option.
5. Developed Spark-specific versions of the get_string_agg and get_field macros to ensure compatibility.
6. Universal Changes
   • Replaced the QUALIFY clause with PostgreSQL-compatible logic to ensure consistent behavior across different target types.
   • Modified the casting syntax to use CAST(x AS data_type) for Spark compatibility.
   • Adapted integration tests by creating Spark-specific source files to accommodate Spark's syntax requirements and limitations.
   • Configured the incremental strategy based on the target type, using 'delete+insert' for Postgres and Redshift, and 'merge' for Spark.
   • Adjusted the handling of ROW_NUMBER() in tests to account for Spark's non-deterministic behavior.

What type of PR is this? (check all applicable)

• [x] 🍕 Feature
• [ ] 🐛 Bug Fix
• [ ] 📝 Documentation Update
• [ ] 🎨 Style
• [x] 🧑‍💻 Code Refactor
• [ ] 🔥 Performance Improvements
• [ ] ✅ Test
• [ ] 🤖 Build
• [x] 🔁 CI
• [ ] 📦 Chore (Release)
• [ ] ⏩ Revert

Related Tickets & Documents

Data Lakes Epic

Checklist

• [x] 💣 Is your change a breaking change?
• [ ] 📖 I have updated the CHANGELOG.md

Added tests?

• [X] 👍 yes
• [ ] 🙅 no, because they aren't needed
• [ ] 🙋 no, because I need help

Added to documentation?

• [ ] 📓 internal package docs (ymls, macros, readme, if applicable)
• [ ] 📕 I have raised a Snowplow documentation PR if applicable (Link here if required)
• [ ] 🙅 no documentation needed

[optional] What gif best describes this PR or how it makes you feel?