Task runner plugin docs are loaded too eagerly (replace script plugin docs sometimes)

[anna-geller]

Issue description

Nothing major, just a small annoyance that often the task runner plugin docs takes precedence when it shouldn't. To reproduce, try to navigate to any script plugin property defined after task runner -- instead of seeing script plugin docs, you'll see task runner docs.

Demo https://share.descript.com/view/p5dGR4nODx7

reproducer - navigate the cursor to script or commands and open plugin docs. You'll see task runner docs even though you expect to see dbt or python docs:

id: dwh_and_analytics
namespace: tutorial

tasks:
  - id: dbt
    type: io.kestra.plugin.core.flow.WorkingDirectory
    tasks:
      - id: clone_repository
        type: io.kestra.plugin.git.Clone
        url: https://github.com/kestra-io/dbt-demo
        branch: main
      - id: dbt_build
        type: io.kestra.plugin.dbt.cli.DbtCLI
        taskRunner:
          type: io.kestra.plugin.scripts.runner.docker.Docker
        containerImage: ghcr.io/kestra-io/dbt-duckdb:latest
        commands:
          - dbt deps
          - dbt build
        profiles: |
          jaffle_shop:
            outputs:
              dev:
                type: duckdb
                path: dbt.duckdb
                extensions: 
                  - parquet
                fixed_retries: 1
                threads: 16
                timeout_seconds: 300
            target: dev      
      - id: python
        type: io.kestra.plugin.scripts.python.Script
        outputFiles:
          - "*.csv"
        taskRunner:
          type: io.kestra.plugin.scripts.runner.docker.Docker
        containerImage: ghcr.io/kestra-io/duckdb:latest
        script: >
          import duckdb
          import pandas as pd

          conn = duckdb.connect(database='dbt.duckdb', read_only=False)

          tables_query = "SELECT table_name FROM information_schema.tables WHERE
          table_schema = 'main';"

          tables = conn.execute(tables_query).fetchall()

          # Export each table to CSV, excluding tables that start with 'raw' or
          'stg'

          for table_name in tables:
              table_name = table_name[0]
              # Skip tables with names starting with 'raw' or 'stg'
              if not table_name.startswith('raw') and not table_name.startswith('stg'):
                  query = f"SELECT * FROM {table_name}"
                  df = conn.execute(query).fetchdf()
                  df.to_csv(f"{table_name}.csv", index=False)

          conn.close()