Fix nits

Author: HyukjinKwon

docs/sql-programming-guide.md

@@ -1690,7 +1690,7 @@ using the call `toPandas()` and when creating a Spark DataFrame from a Pandas Da
 the Spark configuration 'spark.sql.execution.arrow.enabled' to 'true'. This is disabled by default.
 
 In addition, optimizations enabled by 'spark.sql.execution.arrow.enabled' could fallback automatically
-to non-optimized implementations if an error occurs before the actual computation within Spark.
+to non-Arrow optimization implementation if an error occurs before the actual computation within Spark.
 This can be controlled by 'spark.sql.execution.arrow.fallback.enabled'.
 
 <div class="codetabs">
@@ -1804,7 +1804,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
 ## Upgrading From Spark SQL 2.3 to 2.4
 
   - Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, `spark.sql.orc.impl` and `spark.sql.orc.filterPushdown` change their default values to `native` and `true` respectively.
-  - In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unabled to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched by `spark.sql.execution.arrow.fallback.enabled`.
+  - In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unabled to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched off by `spark.sql.execution.arrow.fallback.enabled`.
 
 ## Upgrading From Spark SQL 2.2 to 2.3