Can you please post a full minimal example, including code and the resulting figure?

Right, here is a minimal working example that replicates the logic of what I'm doing and produces the apparent bug:

import matplotlib.pylab as plt

#Create synthetic data
axis_values = [x for x in range(1,20)]
height_values = [[x*y for x in axis_values] for y in axis_values]

#See minimal and maximal height values
print(min([min(x) for x in height_values])) #1
print(max([max(x) for x in height_values])) #361

#Color levels and values
color_levels = [5, 10, 50, 100, 300]
color_values = ['#cecece', '#a5a5a5', '#868686', '#656565', '#3b3b3b']

#Create figure
current_figure = plt.figure(figsize = (5.0, 3.0))
axes_1 = current_figure.add_axes([0.0, 0.0, 0.5, 0.5])
axes_2 = current_figure.add_axes([0.5, 0.0, 0.5, 0.5])

plot_1 = axes_1.contourf(axis_values, axis_values, height_values, color_levels, colors = color_values, extend = 'both')
plot_1.cmap.set_over('red')
plot_1.cmap.set_under('blue')

plot_2 = axes_2.contourf(axis_values, axis_values, height_values, color_levels, colors = color_values, extend = 'both')
plot_2.cmap.set_over('red')
plot_2.cmap.set_under('blue')

#Add colorbar
caxes = current_figure.add_axes()
current_figure.colorbar(plot_2, cax = caxes, orientation = 'horizontal')

plt.show(current_figure)

You can easily check that switching to plot_1 results in out of range values on the first set of axes being properly colored in red/blue. Commenting out the colorbar lines removes red/blue colors from both sets of axes.

There is, however, an ugly workaround - adding a phantom colorbar for each set of axes, by inserting the following line,

current_figure.colorbar(plot_1, cax = caxes, orientation = 'horizontal', fraction = 0.0, shrink = 0.0, pad = 0.0, ticks = [])

Reason / Solution

The QuadContourSets do not get notified that the colormap is modified when you do:

plot_1.cmap.set_over('red')
plot_1.cmap.set_under('blue')

Therefore, please add plot_1.changed() after modifying the colormap. (Same with plot_2).

The partial update happens because you add a colorbar to plot_2. This calls plot_2.changed() internally.

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I think this was addressed by the documentation update at #13047.