SNOW-2097586: Add Debug mode for eager evaluation

[sfc-gh-jrose]

1. Which Jira issue is this PR addressing? Make sure that there is an accompanying issue to your PR.

   Fixes SNOW-2097586

2. Fill out the following pre-review checklist:

   • I am adding a new automated test(s) to verify correctness of my new code
     • If this test skips Local Testing mode, I'm requesting review from @snowflakedb/local-testing
   • I am adding new logging messages
   • I am adding a new telemetry message
   • I am adding new credentials
   • I am adding a new dependency
   • If this is a new feature/behavior, I'm adding the Local Testing parity changes.
   • I acknowledge that I have ensured my changes to be thread-safe. Follow the link for more information: Thread-safe Developer Guidelines
   • If adding any arguments to public Snowpark APIs or creating new public Snowpark APIs, I acknowledge that I have ensured my changes include AST support. Follow the link for more information: AST Support Guidelines

3. Please describe how your code solves the related issue.

   This PR adds a new debug mode to sessions. Currently the only difference in debug mode is that dataframe attributes are populated on Dataframe creation instead of lazily, but in the future other features may be added. This change is done as a separate mode in order to avoid the additional describe queries for production pipelines.

The side effect of this change is that errors related to the dataframe schema become apparent on the line of code that caused them rather than on whatever line evaluates the dataframe. I've also modified the exception wrapper for column identifiers to not modify the error message if no additional context is available.

Here's an example of how the user experience changes:

import snowflake.snowpark.functions as f

df = session.create_dataframe(
    [
        ("cat", "1"),
        ("dog", "1"),
        ("cactus", "2"),
        ("coffee", "3"),
    ],
    schema=["object", "type"]
)

category_lookup = session.create_dataframe(
    [
        (1, "Animal"),
        (2, "Plant"),
        (3, "Unknown")],
    schema=["type", "category"]
)

normalized = df.select("OBJECT", f.cast(df.TYPE, IntegerType()))
filtered = normalized.select("OBJECT", "TYPE").where(f.col("TYPE") < 3)
joined = filtered.join(category_lookup, on="TYPE")

# snowflake.snowpark.exceptions.SnowparkSQLException: (1304): 01bc80ed-0e10-2a2a-000c-a909ed766ceb: 000904 (42000): SQL compilation error: error line 1 at position 157
# invalid identifier 'TYPE'
# There are existing quoted column identifiers: []. Please use one of them to reference the column. See more details on Snowflake identifier requirements https://docs.snowflake.com/en/sql-reference/identifiers-syntax

import snowflake.snowpark.context as context
context.enable_debug_mode()

normalized = df.select("OBJECT", f.cast(df.TYPE, IntegerType()))
filtered = normalized.select("OBJECT", "TYPE").where(f.col("TYPE") < 3)
# snowflake.snowpark.exceptions.SnowparkSQLException: (1304): 01bc80ef-0e10-28f2-000c-a909ed7901ff: 000904 (42000): SQL compilation error: error line 1 at position 17
# invalid identifier 'TYPE'
# There are existing quoted column identifiers: ['"OBJECT"', '"CAST (""TYPE"" AS INT)"']. Please use one of them to reference the column. See more details on Snowflake identifier requirements https://docs.snowflake.com/en/sql-reference/identifiers-syntax

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