Let's denote the elementwise/Hadamard and inner/Frobenius products respectively as
$$\eqalign{
A &= B\circ C \cr
\alpha &= B:C = {\rm tr}(B^TC) \cr
}$$
Recall that these products are commutative, and mutually commutative
$$\eqalign{
A\circ B &= B\circ A \cr
A:B &= B:A \cr
A:B\circ C &= A\circ B:C \cr
}$$ and that the matrix of all ones is the identity element for the Hadamard product. Note that the matrices $(A,B,C)$ must have the same shape for these products to make sense.
Your scalar function can be written as
$$\eqalign{
y
&= 1:f \cr
&= 1:(Ax)\circ(Bx) \cr
&= Ax:Bx \cr
}$$
Whosee differential and gradient are
$$\eqalign{
dy
&= A\,dx:Bx + Ax:B\,dx \cr
&= Bx:A\,dx + Ax:B\,dx \cr
&= (A^TB + B^TA)\,x:dx \cr
\cr
\frac{\partial y}{\partial x}
&= (A^TB + B^TA)\,x \cr\cr
}$$
(Note that I've used juxtaposition rather than $*$ for the ordinary matrix product.)
First some notation. Denote the trace/Frobenius product with a colon, i.e.
$$A:B = {\rm Tr}(A^TB)$$
a matrix with an uppercase letter, a vector with a lowercase letter, and a scalar with a Greek letter.
For typing convenience, define
the column vectors
$$\eqalign{
a &= A^T, \quad
c &= C^T, \quad
f &= F, \quad
x &= X \\
}$$
and
the matrices
$$\eqalign{
H &= B^T\big(E\odot af^T\big), \quad
K &= H\odot D \\
}$$
Rewrite the function in terms of these new variables.
$$\eqalign{
\gamma
&= a^T\big(B(xc^T\odot D)\odot E\big)f \\
&= a:\big(B(xc^T\odot D)\odot E\big)f \\
&= af^T:\big(B(xc^T\odot D)\odot E\big) \\
&= (E\odot af^T):B(xc^T\odot D) \\
&= H:(xc^T\odot D) \\
&= K:xc^T \\
&= Kc:x \\
}$$
Now it's a simple matter to find the differential and gradient.
$$\eqalign{
d\gamma &= Kc:dx \\
\frac{\partial \gamma}{\partial x} &= Kc \\
}$$
NB: The properties of the trace allow Frobenius products to be rearranged in a variety of ways.
$$\eqalign{
A:B &= A^T:B^T \\
A:BC &= AC^T:B \;=\; B^TA:C \\
}$$
Also, Hadamard and Frobenius products commute with themselves and each other.
$$\eqalign{
A:B &= B:A \\
A\odot B &= B\odot A \\
C:A\odot B &= C\odot A:B \\
}$$
Update
There was a question in the comments about the related vector-valued problem
$$y = A\big(B(xc^T\odot D)\odot E\big)f$$
Even for this modified problem, the chain rule remains impractical. The real difficulty with both problems is the presence of the Hadamard products $-$ they make things awkward.
Nonetheless, here is how to calculate the gradient of the modified problem.
First, define some new variables.
$$\eqalign{
C &= {\rm Diag}(c), \quad X = {\rm Diag}(x)\;
\implies\;B(xc^T\odot D) = B(XDC) \\
E &= \sum_k \sigma_ku_kv_k^T \quad {\rm \{SVD\}} \\
W_k &= {\rm Diag}(\sigma_ku_k), \; V_k = {\rm Diag}(v_k) \implies
E\odot Z = \sum_k W_k Z V_k \\
}$$
Then rewrite the function.
$$\eqalign{
y &= A(E\odot BXDC)\,f \\
&= \sum_k A(W_kBXDCV_k)f \\
&= \sum_k {\rm vec}\Big(AW_kB\quad{\rm Diag}(x)\quad DCV_kf\Big) \\
&= \sum_k {\rm vec}\Big(\alpha_k\,{\rm Diag}(x)\,\beta_k\Big) \\
&= Jx\\
}$$
where this result provides a closed-form expression for the $J$-matrix.
$$\eqalign{
J &= \sum_k (\beta_k^T\otimes {\tt 1})\odot({\tt 1}\otimes \alpha_k) \\
}$$
Having rewritten the problem in this form, the gradient (i.e. Jacobian) is trivial.
$$\eqalign{
\frac{\partial y}{\partial x} &= J \\
}$$
Best Answer
The Hadamard product of two vectors is like a scalar product but without summing over the index. So in your "my solution" the index m should be i (but there is no sum over i). The last expression in that calculation is then correct.
You cannot rewrite this in your very last expression since there is no sum over i. If you take the directional derivative, i.e. the derivative in the direction of some vector $h$ then you may rewrite it avoiding the index notation: $$ \frac{\partial(K u \circ Tu)}{\partial u} . h = Kh \circ Tu + Ku \circ Th$$