It is not shrinking to zero
When estimated coefficients are zero in Ridge regression, then it is only because a parameter value crossed the value of 0. It is not because it shrank to zero, when the penalty is further increased the coefficient won't remain zero.
Below is an example image for showing that the difference between Lasso (shrinking to zero) and Ridge regression (crossing zero).
In the Lasso regression on the left you see that, as we increase the level of regularisation (reduce the $\ell_1$ norm) more and more coefficients will be zero and they remain zero when we increase the level of regularisation further.
In the Ridge regression on the right, the coefficient related to the green curve is shortly equal to zero when the $\ell_1$ norm of the coefficients is around 0.55. But the coefficient only passes zero and for stronger regularisation levels it is non-zero again.
The reason that coefficients can be zero with ridge regression is because the ridge regression follows a curved path. See Intuition for nonmonotonicity of coefficient paths in ridge regression
The probability of a zero coefficient
The above is more like a semantic comment about the terminology and meaning of 'shrinking'. If we ignore that, then you might still wonder why a zero coefficient is almost surely not occuring (it can occur but the probability is just zero).
This is similar to your other question Why in the Ridge regression, the coefficients cannot be 0? and is similar to $P[X=x]=0$ when $X$ is a continuous variable But here you are looking for a geometric interpretation, based on your drawing.
If we look at the case of Lasso, then we see that there are many circles that touch the square in the points/corners where one of the coefficients is zero. There is a range values for which this occurs and that range has a non-zero probability.
In the case of ridge there are only four possible single points such that the circles touch a circle in a points/corners where one of the coefficients is zero. For these single points there is zero probability that you have these as the OLS solution.