Fix databricks doc for limitations. (#4755)

Signed-off-by: Hao Zhu <hazhu@nvidia.com>

docs/get-started/getting-started-databricks.md

@@ -26,12 +26,12 @@ The number of GPUs per node dictates the number of Spark executors that can run
 1. Adaptive query execution(AQE) and Delta optimization write do not work. These should be disabled
 when using the plugin. Queries may still see significant speedups even with AQE disabled.
 
-    ```bash 
-    spark.databricks.delta.optimizeWrite.enabled false
-    spark.sql.adaptive.enabled false
-    ```
+   ```bash 
+   spark.databricks.delta.optimizeWrite.enabled false
+   spark.sql.adaptive.enabled false
+   ```
 
-    See [issue-1059](https://github.com/NVIDIA/spark-rapids/issues/1059) for more detail. 
+   See [issue-1059](https://github.com/NVIDIA/spark-rapids/issues/1059) for more detail. 
 
 2. Dynamic partition pruning(DPP) does not work.  This results in poor performance for queries which
    would normally benefit from DPP.  See
@@ -42,10 +42,10 @@ when using the plugin. Queries may still see significant speedups even with AQE
 
 4. Cannot spin off multiple executors on a multi-GPU node. 
 
-	Even though it is possible to set `spark.executor.resource.gpu.amount=N` (where N is the number
-    of GPUs per node) in the in Spark Configuration tab, Databricks overrides this to
-    `spark.executor.resource.gpu.amount=1`.  This will result in failed executors when starting the
-    cluster.
+   Even though it is possible to set `spark.executor.resource.gpu.amount=1` in the in Spark 
+   Configuration tab, Databricks overrides this to `spark.executor.resource.gpu.amount=N` 
+   (where N is the number of GPUs per node). This will result in failed executors when starting the
+   cluster.
 
 5. Databricks makes changes to the runtime without notification.