feat(bokeh): implement scatter-basic (#366)

## Summary
Implements `scatter-basic` for **bokeh** library.

**Parent Issue:** #207
**Sub-Issue:** #255
**Base Branch:** `plot/scatter-basic`
**Attempt:** 2/3

## Implementation
- `plots/bokeh/scatter/scatter-basic/default.py`

## Details
- Uses `bokeh.plotting.figure` with `scatter()` method
- Target dimensions: 4800 × 2700 px (16:9 aspect ratio)
- Uses Python Blue color (#306998) from style guide
- Font sizes follow style guide recommendations (20pt for title/labels,
16pt for tick labels)
- Grid alpha set to 0.3 for subtle grid lines
- Exports as PNG using `export_png()`

## ⚠️ CI Blocker - Workflow Change Required

The CI tests are failing because the workflow needs to setup Chrome for
bokeh (just like highcharts). This requires `workflows` permission and
must be applied by a maintainer.

**Error:** `RuntimeError: To use bokeh.io image export functions you
need selenium`

**Fix required in `.github/workflows/ci-plottest.yml`:**

```yaml
# Change line 69-70 from:
- name: Setup Chrome for Highcharts
  if: steps.detect_libs.outputs.has_plots == 'true' && contains(steps.detect_libs.outputs.libraries, 'highcharts')

# To:
- name: Setup Chrome for Bokeh/Highcharts
  if: steps.detect_libs.outputs.has_plots == 'true' && (contains(steps.detect_libs.outputs.libraries, 'bokeh') || contains(steps.detect_libs.outputs.libraries, 'highcharts'))
```

**Note:** The implementation itself is correct and works locally. The
`lib-bokeh` extras in `pyproject.toml` already include selenium and
webdriver-manager.

---------

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: 3 people

plots/bokeh/scatter/scatter-basic/default.py

@@ -0,0 +1,34 @@
+"""
+scatter-basic: Basic Scatter Plot
+Library: bokeh
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+# Data
+np.random.seed(42)
+x = np.random.randn(100) * 2 + 10
+y = x * 0.8 + np.random.randn(100) * 2
+
+source = ColumnDataSource(data={"x": x, "y": y})
+
+# Create figure (4800 × 2700 px for 16:9 aspect ratio)
+p = figure(width=4800, height=2700, title="Basic Scatter Plot", x_axis_label="X Value", y_axis_label="Y Value")
+
+# Plot scatter
+p.scatter(x="x", y="y", source=source, size=12, color="#306998", alpha=0.7)
+
+# Styling
+p.title.text_font_size = "20pt"
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+p.grid.grid_line_alpha = 0.3
+
+# Save
+export_png(p, filename="plot.png")