user357151 showed this isn't true for equivalent metrics in general.
However, if we restrict ourselves to metrics induced by equivalent norms, we get an interesting relation.
Consider $T:V \to V$, where $V$ is a normed vector space with two equivalent norms, say $||\cdot||_1$ and $||\cdot||_2$. Then, there exists some positive constants $a,b$ such that:
$$ a ||x||_1 \le ||x||_2 \le b ||x||_1 \text{ for all }x\in V $$
Additionally, assume that $d_1$ and $d_2$ are induced by the first and second norm respectively, and that we have
$$ d_1( T(x), T(y)) \le c d_1(x, y) $$
We have the relation
$$ \begin{align*}
d_2(T(x),T(y) ) &\le b\times c \times d_1(x,y) \\
&\le \frac{b}{a} \times c \times d_2(x,y)\\
&= c_* d_2(x,y) \end{align*} $$
with $c_* = \frac{b}{a} \times c$.
My result was obtained with the help of this proof.
Hence we may suspect we can lose the contraction properties if the ratio $b/a$ is large enough. This is indeed the case.
Consider the following function $f: \mathbb{R}^2 \to \mathbb{R}^2$,
$$f(x) = (.9\max(|x_1|,|x_2|), .9 \max(|x_1|,|x_2|))$$
Then $f$ is a contraction under the metric induced by the maximum norm $d_\infty$, but not under the metric induced by the Manhattan norm $d_1$.
Indeed,
$$ \begin{align*} d_\infty( f(x), f(y) ) &= \max( |f_1(x) - f_1(y)|, |f_2(x) - f_2(y)| ) \\
&= .9|\max(|x_1|,|x_2|) - \max(|y_1|,|y_2|)|
\\
&\le .9\max(|x_1 - y_1|,|x_2 - y_2|) \\
&= .9 d_\infty(x, y)\end{align*}
$$
where the inequality is obtained from this relation. Note that $c = .9$.
However, $f$ is not a contraction for a metric induced by the Manhattan norm $d_1$. For example, taking $x = (1,0)$, $y = (0,0)$, we have
$$\begin{align*} d_1(x,y) &= 1 + 0 = 1 \\
d_1(f(x),f(y)) &= .9 + .9 = 1.8 \end{align*}$$
which proves that $f$ is not a contraction.
Note that for vectors of length 2, the maximum and Manhattan norms follow the following relation,
$$ ||x||_\infty \le ||x||_1 \le 2 ||x||_\infty $$
$b = 2$ and $a = 1$, and so $c_* = 1.8$.
Similarly,
Consider the following function $g: \mathbb{R}^2 \to \mathbb{R}^2$,
$$g(x) = (.7(|x_1|+|x_2|), 0)$$
Then $g$ is a contraction under the metric induced by the Manhattan norm $d_1$, but not under the metric induced by the maximum norm $d_\infty$
We have
$$ \begin{align*} d_1(g(x),g(y)) &= .7(||x_1| - |y_1|| + ||x_2| - |y_2||) \\
&\le .7(|x_1 - y_1| + |x_2 - y_2|) \\
&= .7 d_1(x,y)
\end{align*}
$$
but we get, with $x = (1,1)$ and $y=(0,0)$,
$$d_\infty(g(x),g(y)) = 2 \times .7 = 1.4 d_\infty(x,y)$$
Again, that's because
$$ .5||x||_1 \le ||x||_\infty \le ||x||_1 $$