Why is there a negative bias in this example of omitted variable bias?

I was learning the mechanics of omitted variable bias in the context of linear regression. I built the following simple model with R:

x_female = runif(100, 1.1, 1.4)
x_male = runif(100, 2.8, 3.1)
y_female = -5 + 5 * x_female + rnorm(100, 0, 0.3)
y_male = -10 + 5 * x_male + rnorm(100, 0, 0.3)
data = data.frame(edu = c(x_female, x_male),
income = c(y_female, y_male),
male = c(rep(0, 100), rep(1, 100)))


The corresponding scatterplot looks like this:

My question is: since gender has a positive correlation with education (male population are better educated), and since gender has a positive effect on income, according to what I read, omitting gender should introduce a positive bias on the estimate of the coefficient of income. However, this isn't the case here - apparently the red/blue regression line (which controls for gender) is steeper than the green regression line (which doesn't). Is there something wrong with my understanding of omitted variable bias?