To tackle this problem, we first need to understand and translate the given natural language description into a symbolic representation that can be used in optimization modeling. The variables are 'milligrams of vitamin B6' and 'milligrams of potassium', which we will denote as $x_1$ and $x_2$, respectively.

The objective function described is: $1.66 \times x_1^2 + 1.68 \times x_1 \times x_2$. This is what we aim to minimize.

Given constraints can be translated into symbolic form as follows:
- Kidney support index for vitamin B6: $4x_1$
- Energy stability index for vitamin B6: $3x_1$
- Kidney support index for potassium: $x_2$
- Energy stability index for potassium: $13x_2$
- Combined kidney support index must be at least 25: $4x_1 + x_2 \geq 25$
- Combined energy stability index must be at least 10: $3x_1 + 13x_2 \geq 10$
- Another constraint given is: $-9x_1 + 5x_2 \geq 0$
- Maximum combined kidney support index: $4x_1 + x_2 \leq 37$
- Maximum combined energy stability index: $3x_1 + 13x_2 \leq 41$

The symbolic representation of the problem is thus:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of potassium')],
    'objective_function': '1.66*x1**2 + 1.68*x1*x2',
    'constraints': [
        '4*x1 + x2 >= 25',
        '3*x1 + 13*x2 >= 10',
        '-9*x1 + 5*x2 >= 0',
        '4*x1 + x2 <= 37',
        '3*x1 + 13*x2 <= 41'
    ]
}
```

Now, let's implement this optimization problem using Gurobi Python. We need to ensure that $x_1$ is an integer variable and $x_2$ can be a continuous variable.

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Add variables: x1 (integer), x2 (continuous)
x1 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_B6")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_potassium")

# Objective function
m.setObjective(1.66*x1**2 + 1.68*x1*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(4*x1 + x2 >= 25, "kidney_support_index_min")
m.addConstr(3*x1 + 13*x2 >= 10, "energy_stability_index_min")
m.addConstr(-9*x1 + 5*x2 >= 0, "constraint_1")
m.addConstr(4*x1 + x2 <= 37, "kidney_support_index_max")
m.addConstr(3*x1 + 13*x2 <= 41, "energy_stability_index_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B6: {x1.x}")
    print(f"Milligrams of Potassium: {x2.x}")
else:
    print("No optimal solution found.")
```