Features Map convolutional neural network I have a question about convolutional neural newtork.

Consider this image: conv example

We have a part of an input matrix and a filter. Ok, now we can do the convolution and the result is a scalar, if it is a large number the future was found otherwise no. So, the features map is a matrix where each number indicates the points where a feature was found. I understood this.
The output of this convolution is a features map (after activation function).
My misunderstanding start here.

The next convolution, will find another feature. I don't understand how this filter, can find a new feature from a representation of where the previous feature was found. The feature maps, output of the first convolution is a rappresentation of indicates only where the features were found.

Shouldn't be the features found instead of a number that indicates how much was found this feature??
How exactly does this work?
Since the kernel size is limited. A feature can encompass more than 3 pixels or say 5 pixels, for that we cannot find it in 1 layer. So by adding 2 layers and having an activation function that is not like a step function which you would prefer, we extend the feature to 5 or 9 pixels. And with n layers, a feature covering 2n+1, or 4n+1 can be seen.

PS. You have some spelling errors. Take your time to frame well. Welcome!