I will only cover the case $n \ge 6$. I will also adopt the convention that for every variable
with subscript, the subscript is aliased modulus $n$. ie. $a_{n+1} = a_1, b_{n+2} = b_2, c_{0} = c_{n}, d_{-1} = d_{n-1}$.
Consider the problem of maximizing following expression
$$F(x_1,x_2,\ldots,x_n) = \sum_{k,cyc} (x_k^3 - x_k x_{k+1} x_{k+2})\tag{*1}$$
as $(x_1, x_2, \ldots, x_n)$ varies over the domain $D = [0,1] \times [0,2] \times \cdots \times [0,n]$.
Since $D$ is compact and $\Delta(\cdot)$ is continuous over $D$, $\Delta(\cdot)$ reaches its maximum at some point $(p_1,p_2,\ldots,p_n) \in D$. For each integer $k : 1 \le k \le n$, consider the function $\phi_k$ defined by
$$[0,k] \ni t \quad\mapsto\quad \phi_k(t) = F(p_1,\ldots,p_{k-1}, t, p_{k+1}, \ldots, p_n ) \in \mathbb{R}$$
It is clear $\phi_k(t)$ has the form
$$t^3 - t(p_{k-1}p_{k-2} + p_{k-1}p_{k+1} + p_{k+1}p_{k+2}) + \text{ terms independent of } t.$$
Since $\;\frac{d^2}{dt^2}\!\phi_k(t) = 6 t > 0\;$ over $(0,k]$, $\phi_k(t)$ is a strictly convex function there. This means $\phi_k(t)$ cannot reaches maximum at the interior of $[0,k]$. As a consequence,
$$p_k = k \;\text{ or }\; 0 \quad\text{ for }\quad k = 1,2,\ldots,n$$
This means in general, if we want to search for a maximum of $(*1)$, we only need to
look at the $2^n$ vertices of its domain $D$.
We can actually do better than that.
Consider the differences of $\phi_k(t)$ at $t = 0$ and $t = k$:
$$\Delta_k = \phi_k(k) - \phi_k(0) = k\left[ k^2 - (p_{k-2}p_{k-1} + p_{k-1}p_{k+1} + p_{k+1}p_{k+2}) \right]\tag{*2}$$
By definition of $p_k$, $t = p_k$ is a maximum of $\phi_k(t)$. This implies
$$\Delta_k \begin{cases} \ge 0, & p_k = k\\ \le 0, & p_k = 0\end{cases}$$
if we look at the explicit form of $\Delta_k$ in $(*2)$, we can make several observations:
For any $k : k \le n-2$, if $p_{k+1}, p_{k+2} \ne 0$, then
$$\Delta_k \le k (k^2 - p_{k+1}p_{k+1}) = k(k^2 - (k+1)(k+2)) < 0
\quad\implies\quad p_k = 0$$
For any $k : 3 \le k \le n-1$, if $p_{k+1} = 0$, then
$$\Delta_k = k(k^2 - p_{k-2}p_{k-1}) = k(k^2 - (k-2)(k-1)) > 0
\quad\implies\quad p_k = k$$
Bases on observations $1$ and $2$, we find for any $k : 4 \le k \le n-2$, if
$p_{k+2} = 0$, then $p_{k+1} = k+1$ and one and only one of $p_{k}, p_{k-1}$ vanishes.
To see which one should vanish, we can compare the values of
$$\begin{align}
& F(\ldots,p_{k-2},0,k,p_{k+1},\ldots) - F(\ldots,p_{k-2},0,0,p_{k+1},\ldots)\\
\text{ vs. }\quad &
F(\ldots,p_{k-2},k-1,0,p_{k+1},\ldots) - F(\ldots,p_{k-2},0,0,p_{k+1},\ldots)
\end{align}$$
The corresponding values are
$$k^3 \quad\text{ vs. }\quad (k-1)((k-1)^2 - p_{k-3}p_{k-2})$$
Since RHS > LHS, we can conclude it is $p_{k-1}$ that vanishes.
Notice $$\Delta_n \ge n (n^2 - ((n-2)(n-1) + (n-1) + 2)) = n(2n-3) > 0$$
we have $p_n = n$. By observation 1. and 2. again, we find one and only one of $p_{n-1}$, $p_{n-2}$ vanishes. By a similar analysis as step $3$ above, one find that it is $p_{n-2}$ that vanishes.
Combine all these observations, we can conclude aside from some exceptions at the low end (i.e $k < 4$ ), the zeros of $p_k$ start at $k = n - 2$ at the high end and repeat at regular interval of $3$ towards the lower end. This significantly cut down the possible choices of $p_k$ from $2^n$ to a very small number.
There are still some loose ends. In particular, what are the exact places where $p_k = 0$
at the low end? I'm not going to spend more time to sort out these last details. Let's just say for the special case $n = 25$ that inspire this question, the maximum is occurred at
$$(p_1,\ldots,p_{25}) = ( 0, 0, 3, 4, 0, 6, 7, 0, 9, 10, 0,12,13, 0,15,16, 0,18,19, 0,21,22, 0,24,25)$$
with value $75824$. For this particular case, we see the zeros of $p_k$ clearly follows the pattern of repeat at regular interval of $3$ except at the low end. At $k = 1$ and $2$, this pattern breaks with a pair of repeated zeroes.