To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each of the objects mentioned (milligrams of vitamin B6, milligrams of vitamin B4, and milligrams of vitamin C), formulating the objective function using these variables, and then listing out all the constraints as described.

Let's denote:
- \(x_1\) as the milligrams of vitamin B6,
- \(x_2\) as the milligrams of vitamin B4,
- \(x_3\) as the milligrams of vitamin C.

The objective function to maximize is: \(8x_1 + 3x_2 + 3x_3\).

Given constraints:
1. The immune support index of milligrams of vitamin B6 is 32, so \(32x_1\).
2. The immune support index of milligrams of vitamin B4 is 12, so \(12x_2\).
3. Milligrams of vitamin C have an immune support index of 19, so \(19x_3\).
4. Total combined immune support index from milligrams of vitamin B6 and vitamin B4 should be greater than or equal to 35: \(32x_1 + 12x_2 \geq 35\).
5. The total combined immune support index from milligrams of vitamin B4 plus milligrams of vitamin C must be 74 or more: \(12x_2 + 19x_3 \geq 74\).
6. Total combined immune support index from milligrams of vitamin B6 and milligrams of vitamin C should be less than or equal to 228: \(32x_1 + 19x_3 \leq 228\).
7. The total combined immune support index from milligrams of vitamin B6 and milligrams of vitamin B4 should be 145 at a maximum: \(32x_1 + 12x_2 \leq 145\).
8. Total combined immune support index from milligrams of vitamin B6, milligrams of vitamin B4, and milligrams of vitamin C should be 145 or less: \(32x_1 + 12x_2 + 19x_3 \leq 145\).

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B6'), ('x2', 'milligrams of vitamin B4'), ('x3', 'milligrams of vitamin C')],
    'objective_function': '8*x1 + 3*x2 + 3*x3',
    'constraints': [
        '32*x1 + 12*x2 >= 35',
        '12*x2 + 19*x3 >= 74',
        '32*x1 + 19*x3 <= 228',
        '32*x1 + 12*x2 <= 145',
        '32*x1 + 12*x2 + 19*x3 <= 145'
    ]
}
```

Now, let's write the Gurobi code for this problem:
```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(lb=0, name="milligrams_of_vitamin_B6")
x2 = m.addVar(lb=0, name="milligrams_of_vitamin_B4")
x3 = m.addVar(lb=0, name="milligrams_of_vitamin_C")

# Set the objective function
m.setObjective(8*x1 + 3*x2 + 3*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(32*x1 + 12*x2 >= 35, "immune_support_B6_B4")
m.addConstr(12*x2 + 19*x3 >= 74, "immune_support_B4_C")
m.addConstr(32*x1 + 19*x3 <= 228, "max_immune_support_B6_C")
m.addConstr(32*x1 + 12*x2 <= 145, "max_immune_support_B6_B4")
m.addConstr(32*x1 + 12*x2 + 19*x3 <= 145, "total_max_immune_support")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B6: {x1.x}")
    print(f"Milligrams of Vitamin B4: {x2.x}")
    print(f"Milligrams of Vitamin C: {x3.x}")
else:
    print("No optimal solution found")
```