## Step 1: Define the symbolic representation of the problem
The problem involves variables 'milligrams of vitamin B1', 'milligrams of vitamin B2', and 'milligrams of vitamin E'. Let's denote these as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $3.28x_0 + 1.23x_1 + 9.04x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $11x_0 \leq 126$ (kidney support index for $x_0$),
2. $11x_0 \leq 142$ (digestive support index for $x_0$),
3. $2x_1 \leq 126$ (kidney support index for $x_1$),
4. $5x_1 \leq 142$ (digestive support index for $x_1$),
5. $7x_2 \leq 126$ (kidney support index for $x_2$),
6. $8x_2 \leq 142$ (digestive support index for $x_2$),
7. $11x_0 + 7x_2 \geq 20$ (total kidney support index from $x_0$ and $x_2$),
8. $11x_0 + 2x_1 + 7x_2 \geq 20$ (total kidney support index from $x_0$, $x_1$, and $x_2$),
9. $11x_0 + 8x_2 \geq 28$ (total digestive support index from $x_0$ and $x_2$),
10. $5x_1 + 8x_2 \geq 24$ (total digestive support index from $x_1$ and $x_2$),
11. $11x_0 + 5x_1 + 8x_2 \geq 24$ (total digestive support index from $x_0$, $x_1$, and $x_2$),
12. $2x_0 - 7x_1 \geq 0$,
13. $-7x_1 + 2x_2 \geq 0$,
14. $11x_0 + 5x_1 \leq 76$ (total digestive support index from $x_0$ and $x_1$),
15. $5x_1 + 8x_2 \leq 65$ (total digestive support index from $x_1$ and $x_2$).

## 4: Consider variable restrictions
- $x_0$ and $x_1$ can be non-integer,
- $x_2$ must be an integer.

## 5: Formulate the problem in Gurobi
We will use Gurobi to solve this optimization problem.

```python
import gurobi as gp

# Define the model
model = gp.Model("vitamin_problem")

# Define the variables
x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin B1
x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B2
x2 = model.addVar(name="x2", lb=0, type=gp.GRB.INTEGER)  # milligrams of vitamin E

# Objective function
model.setObjective(3.28*x0 + 1.23*x1 + 9.04*x2, gp.GRB.MINIMIZE)

# Constraints
model.addConstr(11*x0 <= 126)
model.addConstr(11*x0 <= 142)
model.addConstr(2*x1 <= 126)
model.addConstr(5*x1 <= 142)
model.addConstr(7*x2 <= 126)
model.addConstr(8*x2 <= 142)
model.addConstr(11*x0 + 7*x2 >= 20)
model.addConstr(11*x0 + 2*x1 + 7*x2 >= 20)
model.addConstr(11*x0 + 8*x2 >= 28)
model.addConstr(5*x1 + 8*x2 >= 24)
model.addConstr(11*x0 + 5*x1 + 8*x2 >= 24)
model.addConstr(2*x0 - 7*x1 >= 0)
model.addConstr(-7*x1 + 2*x2 >= 0)
model.addConstr(11*x0 + 5*x1 <= 76)
model.addConstr(5*x1 + 8*x2 <= 65)

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"x0: {x0.varValue}")
    print(f"x1: {x1.varValue}")
    print(f"x2: {x2.varValue}")
    print(f"Objective: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic Representation
The symbolic representation of the problem is as follows:

```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B1'),
        ('x1', 'milligrams of vitamin B2'),
        ('x2', 'milligrams of vitamin E')
    ],
    'objective_function': '3.28*x0 + 1.23*x1 + 9.04*x2',
    'constraints': [
        '11*x0 <= 126',
        '11*x0 <= 142',
        '2*x1 <= 126',
        '5*x1 <= 142',
        '7*x2 <= 126',
        '8*x2 <= 142',
        '11*x0 + 7*x2 >= 20',
        '11*x0 + 2*x1 + 7*x2 >= 20',
        '11*x0 + 8*x2 >= 28',
        '5*x1 + 8*x2 >= 24',
        '11*x0 + 5*x1 + 8*x2 >= 24',
        '2*x0 - 7*x1 >= 0',
        '-7*x1 + 2*x2 >= 0',
        '11*x0 + 5*x1 <= 76',
        '5*x1 + 8*x2 <= 65'
    ]
}
```