## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B6', 'milligrams of vitamin B4', and 'milligrams of vitamin C', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B6'
- $x_2$ for 'milligrams of vitamin B4'
- $x_3$ for 'milligrams of vitamin C'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8x_1 + 3x_2 + 3x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $32x_1 \geq 32$ is not correct, it should be $32x_1 = 32$ but since $x_1$ is a variable representing milligrams, the constraint from the problem statement directly translates to $x_1$ having a specific immune support index, not a constraint in the traditional sense. Similarly, $12x_2 = 12$ and $19x_3 = 19$.
- $32x_1 + 12x_2 \geq 35$
- $12x_2 + 19x_3 \geq 74$
- $32x_1 + 19x_3 \leq 228$
- $32x_1 + 12x_2 \leq 145$
- $32x_1 + 12x_2 + 19x_3 \leq 145$

However, upon closer inspection, it seems the problem statement directly provides the immune support indices for each vitamin, suggesting the constraints should be interpreted as:
- The immune support index of $x_1$ is 32, of $x_2$ is 12, and of $x_3$ is 19.
- Thus, the actual constraints from the problem are:
- $32x_1 + 12x_2 \geq 35$
- $12x_2 + 19x_3 \geq 74$
- $32x_1 + 19x_3 \leq 228$
- $32x_1 + 12x_2 \leq 145$
- $32x_1 + 12x_2 + 19x_3 \leq 145$

## 5: Correct Interpretation of Constraints
Given that $x_1$, $x_2$, and $x_3$ represent milligrams of vitamins B6, B4, and C respectively, and their respective immune support indices are given, we should directly use these indices in forming constraints.

## 6: Formulate the problem in Gurobi
We will use Gurobi to solve this linear programming problem.

## 7: Write the Gurobi Code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name='milligrams_of_vitamin_B6', lb=0)
x2 = model.addVar(name='milligrams_of_vitamin_B4', lb=0)
x3 = model.addVar(name='milligrams_of_vitamin_C', lb=0)

# Objective function
model.setObjective(8*x1 + 3*x2 + 3*x3, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(32*x1 + 12*x2 >= 35)
model.addConstr(12*x2 + 19*x3 >= 74)
model.addConstr(32*x1 + 19*x3 <= 228)
model.addConstr(32*x1 + 12*x2 <= 145)
model.addConstr(32*x1 + 12*x2 + 19*x3 <= 145)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('x1: ', x1.varValue)
    print('x2: ', x2.varValue)
    print('x3: ', x3.varValue)
else:
    print('The model is infeasible')
```

## 8: 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'
    ]
}
```