Cycle through text_color when overlaying Labels

[ahuang11]

Closes #5887

So it's easy to create legends directly in the plot.

And if they need the labels to be black, it's still easy:
hv.Labels().opts(text_color="black")

import pandas as pd
import holoviews as hv

df = pd.DataFrame(
    {
        "City": ["Buenos Aires", "Brasilia", "Santiago", "Bogota", "Caracas"],
        "Country": ["Argentina", "Brazil", "Chile", "Colombia", "Venezuela"],
        "Latitude": [-34.58, -15.78, -33.45, 4.60, 10.48],
        "Longitude": [-58.66, -47.91, -70.66, -74.08, -66.86],
    }
)
hv.Dataset(df).to.labels(["Latitude", "Longitude"], ["City"]).overlay().opts("Labels", text_color="black")

Or just use hv.Text()

[codecov-commenter]

Codecov Report

Merging #5888 (80bc964) into main (0b76098) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main    #5888   +/-   ##
=======================================
  Coverage   88.28%   88.28%           
=======================================
  Files         311      311           
  Lines       64370    64386   +16     
=======================================
+ Hits        56830    56846   +16     
  Misses       7540     7540           

Flag	Coverage Δ
ui-tests	23.36% <64.70%> (+<0.01%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files Changed	Coverage Δ
holoviews/plotting/bokeh/__init__.py	98.05% <100.00%> (ø)
holoviews/plotting/mpl/__init__.py	81.61% <100.00%> (+0.13%)	⬆️
holoviews/plotting/plotly/__init__.py	93.22% <100.00%> (+0.11%)	⬆️
holoviews/tests/plotting/bokeh/test_labels.py	100.00% <100.00%> (ø)
holoviews/tests/plotting/matplotlib/test_labels.py	100.00% <100.00%> (ø)
holoviews/tests/plotting/plotly/test_labelplot.py	100.00% <100.00%> (ø)

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[hoxbro]

Looks good

[github-actions]

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