## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin E', 'milligrams of potassium', 'milligrams of vitamin A']. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is $8.91x_0 + 4.08x_1 + 6.4x_2$.

## Step 3: List the constraints
The constraints given are:
- $3.06x_0 \geq 0$ (implicit, as $x_0$ is non-negative)
- $0.65x_0 \geq 0$ (implicit, as $x_0$ is non-negative)
- $3.33x_1 \geq 0$ (implicit, as $x_1$ is non-negative)
- $0.02x_1 \geq 0$ (implicit, as $x_1$ is non-negative)
- $3.55x_2 \geq 0$ (implicit, as $x_2$ is non-negative)
- $1.68x_2 \geq 0$ (implicit, as $x_2$ is non-negative)
- $3.06x_0 + 3.33x_1 \geq 36$
- $3.33x_1 + 3.55x_2 \geq 50$
- $0.65x_0 + 0.02x_1 + 1.68x_2 \geq 48$
- $3.33x_1 + 3.55x_2 \leq 113$
- $3.06x_0 + 3.33x_1 \leq 114$
- $3.06x_0 + 3.33x_1 + 3.55x_2 \leq 136$
- $0.65x_0 + 1.68x_2 \leq 114$
- $0.65x_0 + 0.02x_1 + 1.68x_2 \leq 114$

## 4: Convert the problem into Gurobi code
We will use Gurobi to solve this linear programming problem.

## 5: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name='x0', lb=0)  # milligrams of vitamin E
x1 = model.addVar(name='x1', lb=0)  # milligrams of potassium
x2 = model.addVar(name='x2', lb=0)  # milligrams of vitamin A

# Define the objective function
model.setObjective(8.91 * x0 + 4.08 * x1 + 6.4 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(3.06 * x0 + 3.33 * x1 >= 36)
model.addConstr(3.33 * x1 + 3.55 * x2 >= 50)
model.addConstr(0.65 * x0 + 0.02 * x1 + 1.68 * x2 >= 48)
model.addConstr(3.33 * x1 + 3.55 * x2 <= 113)
model.addConstr(3.06 * x0 + 3.33 * x1 <= 114)
model.addConstr(3.06 * x0 + 3.33 * x1 + 3.55 * x2 <= 136)
model.addConstr(0.65 * x0 + 1.68 * x2 <= 114)
model.addConstr(0.65 * x0 + 0.02 * x1 + 1.68 * x2 <= 114)

# Add resource constraints
r0_constraint = model.addConstr(3.06 * x0 + 3.33 * x1 + 3.55 * x2 <= 152)
r1_constraint = model.addConstr(0.65 * x0 + 0.02 * x1 + 1.68 * x2 <= 178)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('x0: ', x0.x)
    print('x1: ', x1.x)
    print('x2: ', x2.x)
else:
    print('The model is infeasible')
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin E'),
        ('x1', 'milligrams of potassium'),
        ('x2', 'milligrams of vitamin A')
    ],
    'objective_function': '8.91*x0 + 4.08*x1 + 6.4*x2',
    'constraints': [
        '3.06*x0 + 3.33*x1 >= 36',
        '3.33*x1 + 3.55*x2 >= 50',
        '0.65*x0 + 0.02*x1 + 1.68*x2 >= 48',
        '3.33*x1 + 3.55*x2 <= 113',
        '3.06*x0 + 3.33*x1 <= 114',
        '3.06*x0 + 3.33*x1 + 3.55*x2 <= 136',
        '0.65*x0 + 1.68*x2 <= 114',
        '0.65*x0 + 0.02*x1 + 1.68*x2 <= 114',
        '3.06*x0 + 3.33*x1 + 3.55*x2 <= 152',
        '0.65*x0 + 0.02*x1 + 1.68*x2 <= 178'
    ]
}
```