## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin D', 'milligrams of zinc', and 'milligrams of magnesium', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables and their corresponding natural language objects are:
- $x_0$ : milligrams of vitamin D
- $x_1$ : milligrams of zinc
- $x_2$ : milligrams of magnesium

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7x_0 + 5x_1 + 1x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3x_0 \geq 9$ (kidney support index from $x_0$ and $x_2$)
- $6x_1 + 6x_2 \geq 18$ (kidney support index from $x_1$ and $x_2$)
- $x_0 + 11x_2 \geq 57$ (muscle growth index from $x_0$ and $x_2$)
- $6x_1 + 6x_2 \leq 51$ (kidney support index from $x_1$ and $x_2$)
- $3x_0 + 6x_1 + 6x_2 \leq 57$ (kidney support index from $x_0$, $x_1$, and $x_2$)
- $8x_0 + x_1 \leq 113$ (cognitive performance index from $x_0$ and $x_1$)
- $x_1 + 11x_2 \leq 109$ (cognitive performance index from $x_1$ and $x_2$)
- $8x_0 + x_1 + 11x_2 \leq 109$ (cognitive performance index from $x_0$, $x_1$, and $x_2$)
- $8x_0 + 14x_1 \leq 119$ (energy stability index from $x_0$ and $x_1$)
- $8x_0 + 14x_1 + 12x_2 \leq 119$ (energy stability index from $x_0$, $x_1$, and $x_2$)
- $x_0 + 11x_2 \leq 87$ (muscle growth index from $x_0$ and $x_2$)
- $8x_1 + 11x_2 \leq 159$ (muscle growth index from $x_1$ and $x_2$)
- $x_0 + 8x_1 + 11x_2 \leq 159$ (muscle growth index from $x_0$, $x_1$, and $x_2$)

## 5: Correct and simplify constraints for accurate representation
Correcting and simplifying:
- The constraint $3x_0 \geq 9$ implies $x_0 \geq 3$.
- Other constraints are already in a suitable form.

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin D'), ('x1', 'milligrams of zinc'), ('x2', 'milligrams of magnesium')],
    'objective_function': '7*x0 + 5*x1 + 1*x2',
    'constraints': [
        'x0 >= 3',
        '6*x1 + 6*x2 >= 18',
        'x0 + 11*x2 >= 57',
        '6*x1 + 6*x2 <= 51',
        '3*x0 + 6*x1 + 6*x2 <= 57',
        '8*x0 + x1 <= 113',
        'x1 + 11*x2 <= 109',
        '8*x0 + x1 + 11*x2 <= 109',
        '8*x0 + 14*x1 <= 119',
        '8*x0 + 14*x1 + 12*x2 <= 119',
        'x0 + 11*x2 <= 87',
        '8*x1 + 11*x2 <= 159',
        'x0 + 8*x1 + 11*x2 <= 159'
    ]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of vitamin D")
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of zinc")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of magnesium")

    # Objective function
    model.setObjective(7*x0 + 5*x1 + x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x0 >= 3, name="kidney_support_index_x0")
    model.addConstr(6*x1 + 6*x2 >= 18, name="kidney_support_index_x1_x2")
    model.addConstr(x0 + 11*x2 >= 57, name="muscle_growth_index_x0_x2")
    model.addConstr(6*x1 + 6*x2 <= 51, name="kidney_support_index_x1_x2_upper")
    model.addConstr(3*x0 + 6*x1 + 6*x2 <= 57, name="kidney_support_index_all")
    model.addConstr(8*x0 + x1 <= 113, name="cognitive_performance_index_x0_x1")
    model.addConstr(x1 + 11*x2 <= 109, name="cognitive_performance_index_x1_x2")
    model.addConstr(8*x0 + x1 + 11*x2 <= 109, name="cognitive_performance_index_all")
    model.addConstr(8*x0 + 14*x1 <= 119, name="energy_stability_index_x0_x1")
    model.addConstr(8*x0 + 14*x1 + 12*x2 <= 119, name="energy_stability_index_all")
    model.addConstr(x0 + 11*x2 <= 87, name="muscle_growth_index_x0_x2_upper")
    model.addConstr(8*x1 + 11*x2 <= 159, name="muscle_growth_index_x1_x2")
    model.addConstr(x0 + 8*x1 + 11*x2 <= 159, name="muscle_growth_index_all")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {x0.varValue}")
        print(f"Milligrams of zinc: {x1.varValue}")
        print(f"Milligrams of magnesium: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```