Cannot find the p.d.f. with jacobian transformation.

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Suppose $X$ and $Y$ are continuous random variable with joint probability density function $$f(x,y)= \begin{cases} 12xy(1-x)&0<x<1,0<y<1\\ 0&\text{for other } x \end{cases}. $$ If $Z_1=X^2Y$, determine the probability density function of $Z_1$.

Because of the p.d.f. is consist of two random variables, I am making other transformation $Z_2=X$. So, the domain of $Z_1$ and $Z_2$ are $0<Z_1<1$ and $0<Z_2<1$. Now I find the inverse of $Z_1$ and $Z_2$, i.e. $X$ and $Y$ as below.

$$X=Z_2,$$ $$Y=\dfrac{Z_1}{X^2}=\dfrac{Z_1}{Z_2^2}.$$

Next I find the jacobian determinant, $$ \vert J\vert= \left\vert \begin{matrix} \dfrac{\partial X}{\partial Z_1}&\dfrac{\partial X}{\partial Z_2}\\ \dfrac{\partial Y}{\partial Z_1}&\dfrac{\partial Y}{\partial Z_2}\\ \end{matrix} \right\vert = \vert J\vert= \left\vert \begin{matrix} 0&1\\ \dfrac{1}{Z_2^2}&-2\dfrac{Z_1}{Z_2^3}\\ \end{matrix} \right\vert = \left\vert-\dfrac{1}{Z_2^2}\right\vert = \dfrac{1}{Z_2^2}. $$ Next, I find the joint p.d.f. of $Z_1$ and $Z_2$.

\begin{eqnarray} g(z_1,z_2)&=&f(x,y)\vert J\vert\\ &=&f\left(z_2,\dfrac{z_1}{z_2^2}\right)\vert J\vert\\ &=&12z_2\left(\dfrac{z_1}{z_2^2}\right)\left(1-z_2\right)\dfrac{1}{z_2^2}\\ &=&12\dfrac{z_1}{z_2^3}\left(1-z_2\right). \end{eqnarray} So, the joint p.d.f. of $Z_1$ and $Z_2$ is $$ \begin{cases} 12\dfrac{z_1}{z_2^3}\left(1-z_2\right)&0<z_1<1,0<z_2<1\\ 0&\text{for other } x \end{cases}. $$

Now I want to find the $g(z_1)$, i.e. marginal p.d.f. of $g(z_1,z_2)$.

\begin{eqnarray} g(z_1)&=&\int\limits_{Z_2}g(z_1,z_2)dz_2\\ &=& \int\limits_{0}^1 12\dfrac{z_1}{z_2^3}\left(1-z_2\right) dz_2\\ &=& 12z_1\int\limits_{0}^1 \left(\dfrac{1}{z_2^3}-\dfrac{1}{z_2^2}\right) dz_2\\ &=& 12z_1 \left[-\dfrac{1}{2z_2^2}+\dfrac{1}{z_2}\right]_0^1\\ &=& 12z_1 \left(\dfrac{1}{2}-\infty\right)\\ &=& \infty \end{eqnarray}

My question: Why I found the p.d.f. is infinity? Can this problem be solved? What the mistake for my answer?

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$Z_1$ and $Z_2$ cannot take all values between $0$ and $1$. There is an extra inequality they have to satisfy: $Z_1 =X^{2}Y <X^{2}=Z_2^{2}$. So the joint density vanishes if $z_1 >z_2^{2}$. Now see if yo get the density of $Z_1$ correctly.