[DOC] Clarify about autotermination_minutes in databricks_job clusters (#3641)

* [Docs] Clarify about `autotermination_minutes` in `databricks_job` clusters

Fixes #3640

* Add more unsupported fields

Author: alexott

docs/resources/job.md

@@ -129,7 +129,7 @@ This block describes individual tasks:
 * `depends_on` - (Optional) block specifying dependency(-ies) for a given task.
 * `job_cluster_key` - (Optional) Identifier of the Job cluster specified in the `job_cluster` block.
 * `existing_cluster_id` - (Optional) Identifier of the [interactive cluster](cluster.md) to run job on.  *Note: running tasks on interactive clusters may lead to increased costs!*
-* `new_cluster` - (Optional) Task will run on a dedicated cluster.  See [databricks_cluster](cluster.md) documentation for specification.
+* `new_cluster` - (Optional) Task will run on a dedicated cluster.  See [databricks_cluster](cluster.md) documentation for specification. *Some parameters, such as `autotermination_minutes`, `is_pinned`, `workload_type` aren't supported!*
 * `run_if` - (Optional) An optional value indicating the condition that determines whether the task should be run once its dependencies have been completed. One of `ALL_SUCCESS`, `AT_LEAST_ONE_SUCCESS`, `NONE_FAILED`, `ALL_DONE`, `AT_LEAST_ONE_FAILED` or `ALL_FAILED`. When omitted, defaults to `ALL_SUCCESS`.
 * `retry_on_timeout` - (Optional) (Bool) An optional policy to specify whether to retry a job when it times out. The default behavior is to not retry on timeout.
 * `max_retries` - (Optional) (Integer) An optional maximum number of times to retry an unsuccessful run. A run is considered to be unsuccessful if it completes with a `FAILED` or `INTERNAL_ERROR` lifecycle state. The value -1 means to retry indefinitely and the value 0 means to never retry. The default behavior is to never retry. A run can have the following lifecycle state: `PENDING`, `RUNNING`, `TERMINATING`, `TERMINATED`, `SKIPPED` or `INTERNAL_ERROR`.
@@ -321,7 +321,10 @@ resource "databricks_job" "this" {
 [Shared job cluster](https://docs.databricks.com/jobs.html#use-shared-job-clusters) specification. Allows multiple tasks in the same job run to reuse the cluster.
 
 * `job_cluster_key` - (Required) Identifier that can be referenced in `task` block, so that cluster is shared between tasks
-* `new_cluster` - Same set of parameters as for [databricks_cluster](cluster.md) resource.
+* `new_cluster` - Block with almost the same set of parameters as for [databricks_cluster](cluster.md) resource, except following (check the [REST API documentation for full list of supported parameters](https://docs.databricks.com/api/workspace/jobs/create#job_clusters-new_cluster)):
+  * `autotermination_minutes` - isn't supported
+  * `is_pinned` - isn't supported
+  * `workload_type` - isn't supported
 
 ### schedule Configuration Block