To solve this optimization problem, we need to formulate it as a linear program (LP) and then use Gurobi to find the optimal solution. 

The objective function is: 5 * milligrams of vitamin E + 1 * milligrams of calcium + 8 * milligrams of vitamin C.

We have constraints based on the cardiovascular support index, cognitive performance index, and energy stability index for each nutrient.

Let's define variables:
- x0: milligrams of vitamin E
- x1: milligrams of calcium
- x2: milligrams of vitamin C

Based on the problem description, we can formulate the following constraints:

* Cardiovascular support index constraints:
  - 28 * x0 + 25 * x1 <= 106 (cardiovascular support index from milligrams of vitamin E and milligrams of calcium)
  - 25 * x1 + 13 * x2 <= 70 (cardiovascular support index from milligrams of calcium and milligrams of vitamin C)
  - 28 * x0 + 25 * x1 + 13 * x2 <= 70 (total combined cardiovascular support index)
* Cognitive performance index constraints:
  - 29 * x1 + 18 * x2 <= 148 (cognitive performance index from milligrams of calcium and milligrams of vitamin C)
  - 19 * x0 + 29 * x1 <= 103 (cognitive performance index from milligrams of vitamin E and milligrams of calcium)
  - 19 * x0 + 29 * x1 + 18 * x2 <= 103 (total combined cognitive performance index)
* Energy stability index constraints:
  - 24 * x0 + 29 * x1 + 6 * x2 >= 45 (minimum total combined energy stability index)
  - 24 * x0 + 29 * x1 <= 137 (energy stability index from milligrams of vitamin E and milligrams of calcium)
  - 24 * x0 + 6 * x2 <= 166 (energy stability index from milligrams of vitamin E and milligrams of vitamin C)
  - 29 * x1 + 6 * x2 <= 192 (energy stability index from milligrams of calcium and milligrams of vitamin C)
  - 24 * x0 + 29 * x1 + 6 * x2 <= 192 (total combined energy stability index)

We also have the following constraints for variable types:
- x0 is an integer (non-fractional amount of milligrams of vitamin E)
- x1 and x2 can be continuous (non-whole number of milligrams of calcium and vitamin C)

Now, let's implement this problem in Gurobi:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_E")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_calcium")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_C")

# Define the objective function
m.setObjective(5 * x0 + 1 * x1 + 8 * x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(28 * x0 + 25 * x1 <= 106)
m.addConstr(25 * x1 + 13 * x2 <= 70)
m.addConstr(28 * x0 + 25 * x1 + 13 * x2 <= 70)
m.addConstr(29 * x1 + 18 * x2 <= 148)
m.addConstr(19 * x0 + 29 * x1 <= 103)
m.addConstr(19 * x0 + 29 * x1 + 18 * x2 <= 103)
m.addConstr(24 * x0 + 29 * x1 + 6 * x2 >= 45)
m.addConstr(24 * x0 + 29 * x1 <= 137)
m.addConstr(24 * x0 + 6 * x2 <= 166)
m.addConstr(29 * x1 + 6 * x2 <= 192)
m.addConstr(24 * x0 + 29 * x1 + 6 * x2 <= 192)

# Solve the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of vitamin E: {x0.X}")
    print(f"Milligrams of calcium: {x1.X}")
    print(f"Milligrams of vitamin C: {x2.X}")
else:
    print("No optimal solution found")
```