## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin E', 'milligrams of calcium', and 'milligrams of vitamin C'. Let's denote them 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 is:
- $x_0$ : milligrams of vitamin E
- $x_1$ : milligrams of calcium
- $x_2$ : milligrams of vitamin C

## 3: Define the objective function in symbolic notation
The objective function to maximize is $5x_0 + 1x_1 + 8x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $24x_0 \leq 108$ (cardiovascular support index for $x_0$)
- $19x_0 \leq 250$ (cognitive performance index for $x_0$)
- $24x_0 \leq 251$ (energy stability index for $x_0$)
- $25x_1 \leq 108$ (cardiovascular support index for $x_1$)
- $29x_1 \leq 250$ (cognitive performance index for $x_1$)
- $29x_1 \leq 251$ (energy stability index for $x_1$)
- $13x_2 \leq 108$ (cardiovascular support index for $x_2$)
- $18x_2 \leq 250$ (cognitive performance index for $x_2$)
- $6x_2 \leq 251$ (energy stability index for $x_2$)
- $24x_0 + 29x_1 + 6x_2 \geq 45$ (total combined energy stability index)
- $28x_0 + 25x_1 \leq 106$ (total combined cardiovascular support index for $x_0$ and $x_1$)
- $25x_1 + 13x_2 \leq 70$ (total combined cardiovascular support index for $x_1$ and $x_2$)
- $28x_0 + 25x_1 + 13x_2 \leq 70$ (total combined cardiovascular support index for $x_0$, $x_1$, and $x_2$)
- $29x_1 + 18x_2 \leq 148$ (total combined cognitive performance index for $x_1$ and $x_2$)
- $19x_0 + 29x_1 \leq 103$ (total combined cognitive performance index for $x_0$ and $x_1$)
- $19x_0 + 29x_1 + 18x_2 \leq 103$ (total combined cognitive performance index for $x_0$, $x_1$, and $x_2$)
- $24x_0 + 29x_1 \leq 137$ (total combined energy stability index for $x_0$ and $x_1$)
- $24x_0 + 6x_2 \leq 166$ (total combined energy stability index for $x_0$ and $x_2$)
- $29x_1 + 6x_2 \leq 192$ (total combined energy stability index for $x_1$ and $x_2$)
- $24x_0 + 29x_1 + 6x_2 \leq 192$ (total combined energy stability index for $x_0$, $x_1$, and $x_2$)
- $x_0$ is an integer (non-fractional amount of milligrams of vitamin E)
- $x_1$ is a continuous variable (non-whole number of milligrams of calcium)
- $x_2$ is a continuous variable (non-whole number of milligrams of vitamin C)

## 5: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin E'),
        ('x1', 'milligrams of calcium'),
        ('x2', 'milligrams of vitamin C')
    ],
    'objective_function': '5*x0 + 1*x1 + 8*x2',
    'constraints': [
        '24*x0 <= 108',
        '19*x0 <= 250',
        '24*x0 <= 251',
        '25*x1 <= 108',
        '29*x1 <= 250',
        '29*x1 <= 251',
        '13*x2 <= 108',
        '18*x2 <= 250',
        '6*x2 <= 251',
        '24*x0 + 29*x1 + 6*x2 >= 45',
        '28*x0 + 25*x1 <= 106',
        '25*x1 + 13*x2 <= 70',
        '28*x0 + 25*x1 + 13*x2 <= 70',
        '29*x1 + 18*x2 <= 148',
        '19*x0 + 29*x1 <= 103',
        '19*x0 + 29*x1 + 18*x2 <= 103',
        '24*x0 + 29*x1 <= 137',
        '24*x0 + 6*x2 <= 166',
        '29*x1 + 6*x2 <= 192',
        '24*x0 + 29*x1 + 6*x2 <= 192',
        'x0 % 1 == 0'  # x0 is an integer
    ]
}
```

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

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

    # Define variables
    x0 = model.addVar(name="x0", vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin E
    x1 = model.addVar(name="x1")  # milligrams of calcium
    x2 = model.addVar(name="x2")  # milligrams of vitamin C

    # Objective function
    model.setObjective(5 * x0 + 1 * x1 + 8 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(24 * x0 <= 108)
    model.addConstr(19 * x0 <= 250)
    model.addConstr(24 * x0 <= 251)
    model.addConstr(25 * x1 <= 108)
    model.addConstr(29 * x1 <= 250)
    model.addConstr(29 * x1 <= 251)
    model.addConstr(13 * x2 <= 108)
    model.addConstr(18 * x2 <= 250)
    model.addConstr(6 * x2 <= 251)
    model.addConstr(24 * x0 + 29 * x1 + 6 * x2 >= 45)
    model.addConstr(28 * x0 + 25 * x1 <= 106)
    model.addConstr(25 * x1 + 13 * x2 <= 70)
    model.addConstr(28 * x0 + 25 * x1 + 13 * x2 <= 70)
    model.addConstr(29 * x1 + 18 * x2 <= 148)
    model.addConstr(19 * x0 + 29 * x1 <= 103)
    model.addConstr(19 * x0 + 29 * x1 + 18 * x2 <= 103)
    model.addConstr(24 * x0 + 29 * x1 <= 137)
    model.addConstr(24 * x0 + 6 * x2 <= 166)
    model.addConstr(29 * x1 + 6 * x2 <= 192)
    model.addConstr(24 * x0 + 29 * x1 + 6 * x2 <= 192)

    # Solve the model
    model.optimize()

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

optimize_problem()
```