[SPARK-49640][PS] Apply reservoir sampling in SampledPlotBase

### What changes were proposed in this pull request?
Apply reservoir sampling in `SampledPlotBase`

### Why are the changes needed?
Existing sampling approach has two drawbacks:

1, it needs two jobs to sample `max_rows` rows:

- df.count() to compute `fraction = max_rows / count`
- df.sample(fraction).to_pandas() to do the sampling

2, the df.sample is based on Bernoulli sampling which **cannot** guarantee the sampled size == expected `max_rows`, e.g.
```
In [1]: df = spark.range(10000)

In [2]: [df.sample(0.01).count() for i in range(0, 10)]
Out[2]: [96, 97, 95, 97, 105, 105, 105, 87, 95, 110]
```
The size of sampled data is floating near the target size 10000*0.01=100.
This relative deviation cannot be ignored, when the input dataset is large and the sampling fraction is small.

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

### How was this patch tested?
CI and manually check

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #48105 from zhengruifeng/ps_sampling.

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

python/pyspark/pandas/plot/core.py

@@ -68,19 +68,52 @@ class SampledPlotBase:
     def get_sampled(self, data):
         from pyspark.pandas import DataFrame, Series
 
+        if not isinstance(data, (DataFrame, Series)):
+            raise TypeError("Only DataFrame and Series are supported for plotting.")
+        if isinstance(data, Series):
+            data = data.to_frame()
+
         fraction = get_option("plotting.sample_ratio")
-        if fraction is None:
-            fraction = 1 / (len(data) / get_option("plotting.max_rows"))
-            fraction = min(1.0, fraction)
-        self.fraction = fraction
-
-        if isinstance(data, (DataFrame, Series)):
-            if isinstance(data, Series):
-                data = data.to_frame()
+        if fraction is not None:
+            self.fraction = fraction
             sampled = data._internal.resolved_copy.spark_frame.sample(fraction=self.fraction)
             return DataFrame(data._internal.with_new_sdf(sampled))._to_pandas()
         else:
-            raise TypeError("Only DataFrame and Series are supported for plotting.")
+            from pyspark.sql import Observation
+
+            max_rows = get_option("plotting.max_rows")
+            observation = Observation("ps plotting")
+            sdf = data._internal.resolved_copy.spark_frame.observe(
+                observation, F.count(F.lit(1)).alias("count")
+            )
+
+            rand_col_name = "__ps_plotting_sampled_plot_base_rand__"
+            id_col_name = "__ps_plotting_sampled_plot_base_id__"
+
+            sampled = (
+                sdf.select(
+                    "*",
+                    F.rand().alias(rand_col_name),
+                    F.monotonically_increasing_id().alias(id_col_name),
+                )
+                .sort(rand_col_name)
+                .limit(max_rows + 1)
+                .coalesce(1)
+                .sortWithinPartitions(id_col_name)
+                .drop(rand_col_name, id_col_name)
+            )
+
+            pdf = DataFrame(data._internal.with_new_sdf(sampled))._to_pandas()
+
+            if len(pdf) > max_rows:
+                try:
+                    self.fraction = float(max_rows) / observation.get["count"]
+                except Exception:
+                    pass
+                return pdf[:max_rows]
+            else:
+                self.fraction = 1.0
+                return pdf
 
     def set_result_text(self, ax):
         assert hasattr(self, "fraction")