To solve the given optimization problem, we need to first identify the variables and the objective function. The variables are:

- `x0`: milligrams of vitamin B4
- `x1`: milligrams of potassium
- `x2`: milligrams of vitamin B1
- `x3`: milligrams of vitamin B6

The objective function is to maximize: `2*x0 + 5*x1 + 8*x2 + 9*x3`

Now, let's list the constraints as described in the problem statement:

1. Immune support index from milligrams of vitamin B4 and potassium and vitamin B1 should be at most 156.
2. The immune support index from milligrams of vitamin B4 plus milligrams of potassium plus milligrams of vitamin B6 has to be at most 148.
3. The total combined immune support index from milligrams of potassium, milligrams of vitamin B1, and milligrams of vitamin B6 must be as much or less than 142.
4. The cardiovascular support index for milligrams of potassium plus milligrams of vitamin B1 should be at most 178.
5. The total combined cardiovascular support index from milligrams of vitamin B4 plus milligrams of potassium plus milligrams of vitamin B1 must be at most 96.
6. The total combined immune support index from milligrams of vitamin B4 and milligrams of vitamin B6 should be at minimum 49.
7. The total combined cardiovascular support index from milligrams of vitamin B4 and milligrams of vitamin B6 must be no less than 34.
8. The total combined immune support index from milligrams of potassium, milligrams of vitamin B1 has to be 259 or less.

With these variables and constraints identified, we can now formulate the symbolic representation of the problem:

```json
{
    'sym_variables': [('x0', 'milligrams of vitamin B4'), ('x1', 'milligrams of potassium'), ('x2', 'milligrams of vitamin B1'), ('x3', 'milligrams of vitamin B6')],
    'objective_function': '2*x0 + 5*x1 + 8*x2 + 9*x3',
    'constraints': [
        '6.49*x0 + 15.8*x1 + 13.81*x2 <= 156',
        '6.49*x0 + 15.8*x1 + 7.23*x3 <= 148',
        '15.8*x1 + 13.81*x2 + 7.23*x3 <= 142',
        '0.7*x1 + 4.16*x2 <= 178',
        '2.09*x0 + 0.7*x1 + 4.16*x2 <= 96',
        '6.49*x0 + 7.23*x3 >= 49',
        '2.09*x0 + 9.55*x3 >= 34',
        '15.8*x1 + 13.81*x2 <= 259'
    ]
}
```

Now, we can implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(name="milligrams_of_vitamin_B4")
x1 = m.addVar(name="milligrams_of_potassium")
x2 = m.addVar(name="milligrams_of_vitamin_B1")
x3 = m.addVar(name="milligrams_of_vitamin_B6")

# Define the objective function
m.setObjective(2*x0 + 5*x1 + 8*x2 + 9*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(6.49*x0 + 15.8*x1 + 13.81*x2 <= 156)
m.addConstr(6.49*x0 + 15.8*x1 + 7.23*x3 <= 148)
m.addConstr(15.8*x1 + 13.81*x2 + 7.23*x3 <= 142)
m.addConstr(0.7*x1 + 4.16*x2 <= 178)
m.addConstr(2.09*x0 + 0.7*x1 + 4.16*x2 <= 96)
m.addConstr(6.49*x0 + 7.23*x3 >= 49)
m.addConstr(2.09*x0 + 9.55*x3 >= 34)
m.addConstr(15.8*x1 + 13.81*x2 <= 259)

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B4: {x0.x}")
    print(f"Milligrams of potassium: {x1.x}")
    print(f"Milligrams of vitamin B1: {x2.x}")
    print(f"Milligrams of vitamin B6: {x3.x}")
else:
    print("No optimal solution found.")
```