## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of zinc' and 'milligrams of vitamin B6', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is $8.82x_1 + 1.66x_2$.

## Step 3: List the constraints
The constraints given are:
- $26x_1 \leq 181$
- $7x_1 \leq 223$
- $28x_1 \leq 131$
- $29x_2 \leq 181$
- $4x_2 \leq 223$
- $7x_2 \leq 131$
- $26x_1 + 29x_2 \geq 85$
- $7x_1 + 4x_2 \geq 60$
- $28x_1 + 7x_2 \geq 24$
- $-6x_1 + 4x_2 \geq 0$
- $26x_1 + 29x_2 \leq 158$
- $7x_1 + 4x_2 \leq 119$
- $28x_1 + 7x_2 \leq 89$

## 4: Convert the problem into a Gurobi model
We will use Gurobi to solve this linear programming problem.

## 5: Write the Gurobi code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="milligrams_of_zinc", lb=0)  # No lower bound specified, assuming 0
x2 = model.addVar(name="milligrams_of_vitamin_B6", lb=0)  # No lower bound specified, assuming 0

# Define the objective function
model.setObjective(8.82 * x1 + 1.66 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(26 * x1 <= 181, name="kidney_support_index_zinc")
model.addConstr(7 * x1 <= 223, name="cardiovascular_support_index_zinc")
model.addConstr(28 * x1 <= 131, name="cognitive_performance_index_zinc")
model.addConstr(29 * x2 <= 181, name="kidney_support_index_vitamin_B6")
model.addConstr(4 * x2 <= 223, name="cardiovascular_support_index_vitamin_B6")
model.addConstr(7 * x2 <= 131, name="cognitive_performance_index_vitamin_B6")
model.addConstr(26 * x1 + 29 * x2 >= 85, name="combined_kidney_support_index_min")
model.addConstr(7 * x1 + 4 * x2 >= 60, name="combined_cardiovascular_support_index_min")
model.addConstr(28 * x1 + 7 * x2 >= 24, name="combined_cognitive_performance_index_min")
model.addConstr(-6 * x1 + 4 * x2 >= 0, name="zinc_vitamin_B6_relation")
model.addConstr(26 * x1 + 29 * x2 <= 158, name="combined_kidney_support_index_max")
model.addConstr(7 * x1 + 4 * x2 <= 119, name="combined_cardiovascular_support_index_max")
model.addConstr(28 * x1 + 7 * x2 <= 89, name="combined_cognitive_performance_index_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of zinc: {x1.varValue}")
    print(f"Milligrams of vitamin B6: {x2.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of zinc'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '8.82x1 + 1.66x2',
    'constraints': [
        '26x1 <= 181',
        '7x1 <= 223',
        '28x1 <= 131',
        '29x2 <= 181',
        '4x2 <= 223',
        '7x2 <= 131',
        '26x1 + 29x2 >= 85',
        '7x1 + 4x2 >= 60',
        '28x1 + 7x2 >= 24',
        '-6x1 + 4x2 >= 0',
        '26x1 + 29x2 <= 158',
        '7x1 + 4x2 <= 119',
        '28x1 + 7x2 <= 89'
    ]
}
```