To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

### Symbolic Representation

Let's define:
- $x_0$ as the milligrams of zinc,
- $x_1$ as the milligrams of vitamin B6.

The objective function to maximize is: $8.82x_0 + 1.66x_1$

The constraints are:
1. Kidney support index for zinc: $26x_0$
2. Cardiovascular support index for zinc: $7x_0$
3. Cognitive performance index for zinc: $28x_0$
4. Kidney support index for vitamin B6: $29x_1$
5. Cardiovascular support index for vitamin B6: $4x_1$
6. Cognitive performance index for vitamin B6: $7x_1$
7. Total combined kidney support index minimum: $26x_0 + 29x_1 \geq 85$
8. Total combined cardiovascular support index minimum: $7x_0 + 4x_1 \geq 60$
9. Total combined cognitive performance index minimum: $28x_0 + 7x_1 \geq 24$
10. Linear constraint: $-6x_0 + 4x_1 \geq 0$
11. Total combined kidney support index maximum: $26x_0 + 29x_1 \leq 158$
12. Total combined cardiovascular support index maximum: $7x_0 + 4x_1 \leq 119$
13. Total combined cognitive performance index maximum: $28x_0 + 7x_1 \leq 89$

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x0', 'milligrams of zinc'), ('x1', 'milligrams of vitamin B6')],
    'objective_function': '8.82*x0 + 1.66*x1',
    'constraints': [
        '26*x0 + 29*x1 >= 85',
        '7*x0 + 4*x1 >= 60',
        '28*x0 + 7*x1 >= 24',
        '-6*x0 + 4*x1 >= 0',
        '26*x0 + 29*x1 <= 158',
        '7*x0 + 4*x1 <= 119',
        '28*x0 + 7*x1 <= 89'
    ]
}
```

### Gurobi Code

To solve this problem using Gurobi, we will write the following Python code:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(name="milligrams_of_zinc", lb=-GRB.INFINITY, ub=GRB.INFINITY)
x1 = m.addVar(name="milligrams_of_vitamin_B6", lb=-GRB.INFINITY, ub=GRB.INFINITY)

# Set the objective function
m.setObjective(8.82*x0 + 1.66*x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(26*x0 + 29*x1 >= 85, name="kidney_support_index_min")
m.addConstr(7*x0 + 4*x1 >= 60, name="cardiovascular_support_index_min")
m.addConstr(28*x0 + 7*x1 >= 24, name="cognitive_performance_index_min")
m.addConstr(-6*x0 + 4*x1 >= 0, name="linear_constraint")
m.addConstr(26*x0 + 29*x1 <= 158, name="kidney_support_index_max")
m.addConstr(7*x0 + 4*x1 <= 119, name="cardiovascular_support_index_max")
m.addConstr(28*x0 + 7*x1 <= 89, name="cognitive_performance_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of zinc: {x0.x}")
    print(f"Milligrams of vitamin B6: {x1.x}")
else:
    print("No optimal solution found")
```