Here is a reproducible example how to do it:
using Plots
x = rand(1000)
y = rand(1000)
color = [3 * (b-0.5)^2 < a - 0.1 ? "red" : "blue" for (a, b) in zip(x, y)]
y_bound = 0:0.01:1
x_bound = @. 3 * (y_bound - 0.5)^2 + 0.1
scatter(x, y, color=color, legend=false)
plot!(x_bound, y_bound, color="green")
and you should get a plot like:

The crucial thing here is to make your boundary points ordered (i.e. they must be ordered in the vectors properly so that when you plot a line you connect proper points). In my example I achieved it by varying the y-dimension and calculating the x dimension.
In more complex cases it will be better to use contour plot, e.g.:
x = 1:0.1:8
y = 1:0.1:7
f(x, y) = begin
(3x + y ^ 2) * abs(sin(x) + cos(y)) - 40
end
X = repeat(reshape(x, 1, :), length(y), 1)
Y = repeat(y, 1, length(x))
Z = map(f, X, Y)
contour(x, y, Z, levels=[0], color="green", width=3)
x_s = 7 .* rand(1000) .+ 1
y_s = 6 .* rand(1000) .+ 1
color = [f(a, b) > 0 ? "red" : "blue" for (a, b) in zip(x_s, y_s)]
scatter!(x_s, y_s, color=color, legend=false)
and you should get something like:

However, as you can see this time for the best results it is best to pass scores to contour and specify the classification threshold as level.