[SPARK-54938][PYTHON][TEST][FOLLOW-UP] Fix inferred time unit for pandas >= 3

### What changes were proposed in this pull request?
Fix inferred time unit for pandas >= 3

### Why are the changes needed?
there is behavior change in pandas 3

### Does this PR introduce _any_ user-facing change?
No, test-only

### How was this patch tested?
manually check

pandas=2.3.3
```
In [7]: pd.__version__
Out[7]: '2.3.3'

In [8]: pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"])).dtype
Out[8]: dtype('<M8[ns]')

In [9]: pa.array(pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"]))).type
Out[9]: TimestampType(timestamp[ns])
```

pandas=3.0.1
```
In [6]: pd.__version__
Out[6]: '3.0.1'

In [7]: pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"])).dtype
Out[7]: dtype('<M8[us]')

In [8]: pa.array(pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"]))).type
Out[8]: TimestampType(timestamp[us])
```

### Was this patch authored or co-authored using generative AI tooling?
Co-authored-by: Claude code (Opus 4.6)

Closes #55158 from zhengruifeng/fix-pyarrow-ts-inference.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

Author: zhengruifeng

python/pyspark/tests/upstream/pyarrow/test_pyarrow_array_type_inference.py

@@ -299,6 +299,8 @@ def test_pandas_series_numpy_backed(self):
 
         # pandas >= 3 infers large_string instead of string for object-dtype string Series
         string_type = pa.large_string() if LooseVersion(pd.__version__) >= "3.0.0" else pa.string()
+        # pandas >= 3 defaults to microsecond resolution instead of nanosecond
+        ts_unit = "us" if LooseVersion(pd.__version__) >= "3.0.0" else "ns"
 
         sg = ZoneInfo("Asia/Singapore")
         la = "America/Los_Angeles"
@@ -324,17 +326,17 @@ def test_pandas_series_numpy_backed(self):
             (pd.Series([True, False, True]), pa.bool_()),
             # Temporal
             (pd.Series([date1, date2]), pa.date32()),
-            (pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"])), pa.timestamp("ns")),
-            (pd.Series([pd.Timestamp("1970-01-01")]), pa.timestamp("ns")),
-            (pd.Series([pd.Timestamp.min]), pa.timestamp("ns")),
-            (pd.Series([pd.Timestamp.max]), pa.timestamp("ns")),
-            (pd.Series(pd.to_timedelta(["1 day", "2 hours"])), pa.duration("ns")),
-            (pd.Series([pd.Timedelta(0)]), pa.duration("ns")),
-            (pd.Series([pd.Timedelta.min]), pa.duration("ns")),
-            (pd.Series([pd.Timedelta.max]), pa.duration("ns")),
+            (pd.Series(pd.to_datetime(["2024-01-01", "2024-01-02"])), pa.timestamp(ts_unit)),
+            (pd.Series([pd.Timestamp("1970-01-01")]), pa.timestamp(ts_unit)),
+            (pd.Series([pd.Timestamp.min]), pa.timestamp(ts_unit)),
+            (pd.Series([pd.Timestamp.max]), pa.timestamp(ts_unit)),
+            (pd.Series(pd.to_timedelta(["1 day", "2 hours"])), pa.duration(ts_unit)),
+            (pd.Series([pd.Timedelta(0)]), pa.duration(ts_unit)),
+            (pd.Series([pd.Timedelta.min]), pa.duration(ts_unit)),
+            (pd.Series([pd.Timedelta.max]), pa.duration(ts_unit)),
             # Timezone-aware
-            (pd.Series([dt1_sg, dt2_sg]), pa.timestamp("ns", tz="Asia/Singapore")),
-            (pd.Series([ts1_la, ts2_la]), pa.timestamp("ns", tz=la)),
+            (pd.Series([dt1_sg, dt2_sg]), pa.timestamp(ts_unit, tz="Asia/Singapore")),
+            (pd.Series([ts1_la, ts2_la]), pa.timestamp(ts_unit, tz=la)),
             # Binary
             (pd.Series([b"hello", b"world"]), pa.binary()),
             # Nested