To solve this optimization problem, we first need to understand and symbolically represent the variables, objective function, and constraints as described in the problem statement.

Given variables:
- `x1`: milligrams of vitamin D
- `x2`: milligrams of vitamin B1
- `x3`: milligrams of zinc
- `x4`: milligrams of vitamin B5

Objective Function: Maximize `2.02*x1 + 6.45*x2 + 5.51*x3 + 2.96*x4`

Constraints:
1. `14*x1 + 6*x2 >= 24` (Minimum combined immune support index from vitamin D and vitamin B1)
2. `14*x1 + 16*x4 >= 23` (Minimum combined immune support index from vitamin D and vitamin B5)
3. `14*x1 + 15*x3 <= 75` (Maximum combined immune support index from vitamin D and zinc)
4. `6*x2 + 15*x3 <= 77` (Maximum combined immune support index from vitamin B1 and zinc)
5. `15*x3 + 16*x4 <= 63` (Maximum combined immune support index from zinc and vitamin B5)
6. `14*x1 + 6*x2 + 15*x3 + 16*x4 <= 112` (Maximum total combined immune support index)

Symbolic Representation:
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin D'), 
        ('x2', 'milligrams of vitamin B1'), 
        ('x3', 'milligrams of zinc'), 
        ('x4', 'milligrams of vitamin B5')
    ], 
    'objective_function': 'Maximize 2.02*x1 + 6.45*x2 + 5.51*x3 + 2.96*x4', 
    'constraints': [
        '14*x1 + 6*x2 >= 24',
        '14*x1 + 16*x4 >= 23',
        '14*x1 + 15*x3 <= 75',
        '6*x2 + 15*x3 <= 77',
        '15*x3 + 16*x4 <= 63',
        '14*x1 + 6*x2 + 15*x3 + 16*x4 <= 112'
    ]
}
```

Now, let's express this problem in Gurobi Python code:

```python
from gurobipy import *

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

# Define the variables (all are continuous)
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="vitamin_D")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="vitamin_B1")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="zinc")
x4 = m.addVar(vtype=GRB.CONTINUOUS, name="vitamin_B5")

# Set the objective function
m.setObjective(2.02*x1 + 6.45*x2 + 5.51*x3 + 2.96*x4, GRB.MAXIMIZE)

# Add constraints
m.addConstr(14*x1 + 6*x2 >= 24)
m.addConstr(14*x1 + 16*x4 >= 23)
m.addConstr(14*x1 + 15*x3 <= 75)
m.addConstr(6*x2 + 15*x3 <= 77)
m.addConstr(15*x3 + 16*x4 <= 63)
m.addConstr(14*x1 + 6*x2 + 15*x3 + 16*x4 <= 112)

# Optimize model
m.optimize()

# Print solution
for v in m.getVars():
    print('%s %g' % (v.varName, v.x))

print('Obj: %g' % m.objVal)
```