To solve this optimization problem, we first need to define the variables and the objective function symbolically. Let's denote:
- `x1` as the milligrams of vitamin K,
- `x2` as the milligrams of vitamin A, and
- `x3` as the milligrams of magnesium.

The objective function is to maximize: `2*x1 + 8*x2 + 8*x3`.

Given constraints:
1. Energy stability index for vitamin K: `11.75*x1`.
2. Energy stability index for vitamin A: `6.07*x2`.
3. Energy stability index for magnesium: `5.28*x3`.
4. The total combined energy stability index from milligrams of vitamin K plus milligrams of magnesium has to be at least 29: `11.75*x1 + 5.28*x3 >= 29`.
5. The total combined energy stability index from all three should be at least 17: `11.75*x1 + 6.07*x2 + 5.28*x3 >= 17`.
6. The total combined energy stability index from vitamin A plus magnesium should be at most 34: `6.07*x2 + 5.28*x3 <= 34`.
7. The total combined energy stability index from vitamin K and magnesium has to be 67 or less: `11.75*x1 + 5.28*x3 <= 67`.
8. The total combined energy stability index from all three should be no more than 68: `11.75*x1 + 6.07*x2 + 5.28*x3 <= 68`.

Since the constraints regarding the total energy stability index from all three components being at most 68 and the combination of vitamin K, magnesium, and vitamin A being less than or equal to 68 seem redundant with constraint 8 encompassing both scenarios effectively, we'll ensure our model accounts for these requirements accurately without duplication.

Symbolic representation:
```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'
    ]
}
```

Given this setup, the Gurobi code to solve this optimization problem can be written as follows:

```python
from gurobipy import *

# Create a new model
model = Model("Vitamin_Optimization")

# Define variables
x1 = model.addVar(name="vitamin_k", lb=0)
x2 = model.addVar(name="vitamin_a", lb=0)
x3 = model.addVar(name="magnesium", lb=0)

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

# Add constraints
model.addConstr(11.75*x1 + 5.28*x3 >= 29, name="vitamin_k_magnesium_min")
model.addConstr(11.75*x1 + 6.07*x2 + 5.28*x3 >= 17, name="all_vitamins_min")
model.addConstr(6.07*x2 + 5.28*x3 <= 34, name="vitamin_a_magnesium_max")
model.addConstr(11.75*x1 + 5.28*x3 <= 67, name="vitamin_k_magnesium_max")
model.addConstr(11.75*x1 + 6.07*x2 + 5.28*x3 <= 68, name="all_vitamins_max")

# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin K: {x1.x}")
    print(f"Milligrams of Vitamin A: {x2.x}")
    print(f"Milligrams of Magnesium: {x3.x}")
    print(f"Objective Function Value: {model.objVal}")
else:
    print("No optimal solution found")
```