## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_1$ represents the milligrams of vitamin K
- $x_2$ represents the milligrams of vitamin A
- $x_3$ represents the milligrams of magnesium

The objective function to maximize is $2x_1 + 8x_2 + 8x_3$.

## Step 2: Translate the constraints into symbolic notation
Given:
- The energy stability index for milligrams of vitamin K is 11.75, so $r0_{x_1} = 11.75$
- The energy stability index for milligrams of vitamin A is 6.07, so $r0_{x_2} = 6.07$
- The energy stability index for milligrams of magnesium is 5.28, so $r0_{x_3} = 5.28$

Constraints:
1. $11.75x_1 + 5.28x_3 \geq 29$
2. $11.75x_1 + 6.07x_2 + 5.28x_3 \geq 17$
3. $6.07x_2 + 5.28x_3 \leq 34$
4. $11.75x_1 + 5.28x_3 \leq 67$
5. $11.75x_1 + 6.07x_2 + 5.28x_3 \leq 68$

## 3: Express the problem in a suitable format for Gurobi
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin K'), ('x2', 'milligrams of vitamin A'), ('x3', 'milligrams of magnesium')],
'objective_function': '2*x1 + 8*x2 + 8*x3',
'constraints': [
    '11.75*x1 + 5.28*x3 >= 29',
    '11.75*x1 + 6.07*x2 + 5.28*x3 >= 17',
    '6.07*x2 + 5.28*x3 <= 34',
    '11.75*x1 + 5.28*x3 <= 67',
    '11.75*x1 + 6.07*x2 + 5.28*x3 <= 68'
]
}
```

## 4: Implement the problem using Gurobi
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # Vitamin K
    x2 = model.addVar(name="x2", lb=0)  # Vitamin A
    x3 = model.addVar(name="x3", lb=0)  # Magnesium

    # Define the objective function
    model.setObjective(2*x1 + 8*x2 + 8*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(11.75*x1 + 5.28*x3 >= 29)
    model.addConstr(11.75*x1 + 6.07*x2 + 5.28*x3 >= 17)
    model.addConstr(6.07*x2 + 5.28*x3 <= 34)
    model.addConstr(11.75*x1 + 5.28*x3 <= 67)
    model.addConstr(11.75*x1 + 6.07*x2 + 5.28*x3 <= 68)

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin K: {x1.varValue}")
        print(f"Milligrams of vitamin A: {x2.varValue}")
        print(f"Milligrams of magnesium: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```