## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_0$ represents 'milligrams of vitamin E'
- $x_1$ represents 'milligrams of magnesium'
- $x_2$ represents 'milligrams of vitamin A'

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $2x_0 + 1x_1 + 6x_2$.

## Step 3: List the constraints in symbolic notation
Constraints:
1. $13x_0 + 10x_1 \geq 29$
2. $10x_0 + 22x_1 \geq 52$
3. $10x_0 + 4x_2 \geq 40$
4. $17x_1 + 2x_2 \geq 31$
5. $4x_1 + 17x_2 \geq 42$
6. $8x_0 + 17x_2 \geq 16$
7. $13x_0 + 10x_1 \leq 99$
8. $13x_0 + x_2 \leq 104$
9. $13x_0 + 10x_1 + x_2 \leq 161$
10. $10x_0 + 22x_1 + 4x_2 \leq 130$
11. $10x_0 + 4x_2 \leq 130$
12. $5x_0 + 17x_1 + 2x_2 \leq 73$
13. $7x_0 + 20x_1 + 5x_2 \leq 183$
14. $7x_0 + 20x_1 + 5x_2 \leq 151$
15. $8x_0 + 4x_1 + 17x_2 \leq 106$
16. $8x_0 + 4x_1 \leq 51$
17. $4x_1 + 17x_2 \leq 106$
18. $5x_0 + 17x_1 + 2x_2 \leq 73$

## 4: Create the Gurobi model and variables
We will use Gurobi to solve this optimization problem.

```python
import gurobi

# Create a new Gurobi 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 magnesium
x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin A

# Objective function
model.setObjective(2 * x0 + x1 + 6 * x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(13 * x0 + 10 * x1 >= 29)
model.addConstr(10 * x0 + 22 * x1 >= 52)
model.addConstr(10 * x0 + 4 * x2 >= 40)
model.addConstr(17 * x1 + 2 * x2 >= 31)
model.addConstr(4 * x1 + 17 * x2 >= 42)
model.addConstr(8 * x0 + 17 * x2 >= 16)
model.addConstr(13 * x0 + 10 * x1 <= 99)
model.addConstr(13 * x0 + x2 <= 104)
model.addConstr(13 * x0 + 10 * x1 + x2 <= 161)
model.addConstr(10 * x0 + 22 * x1 + 4 * x2 <= 130)
model.addConstr(10 * x0 + 4 * x2 <= 130)
model.addConstr(5 * x0 + 17 * x1 + 2 * x2 <= 73)
model.addConstr(7 * x0 + 20 * x1 + 5 * x2 <= 183)
model.addConstr(7 * x0 + 20 * x1 + 5 * x2 <= 151)
model.addConstr(8 * x0 + 4 * x1 + 17 * x2 <= 106)
model.addConstr(8 * x0 + 4 * x1 <= 51)
model.addConstr(4 * x1 + 17 * x2 <= 106)
model.addConstr(5 * x0 + 17 * x1 + 2 * x2 <= 73)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin E: {x0.varValue}")
    print(f"Milligrams of magnesium: {x1.varValue}")
    print(f"Milligrams of vitamin A: {x2.varValue}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found.")
```

## 5: Symbolic representation in JSON format
```json
{
    "sym_variables": [
        ["x0", "milligrams of vitamin E"],
        ["x1", "milligrams of magnesium"],
        ["x2", "milligrams of vitamin A"]
    ],
    "objective_function": "2*x0 + x1 + 6*x2",
    "constraints": [
        "13*x0 + 10*x1 >= 29",
        "10*x0 + 22*x1 >= 52",
        "10*x0 + 4*x2 >= 40",
        "17*x1 + 2*x2 >= 31",
        "4*x1 + 17*x2 >= 42",
        "8*x0 + 17*x2 >= 16",
        "13*x0 + 10*x1 <= 99",
        "13*x0 + x2 <= 104",
        "13*x0 + 10*x1 + x2 <= 161",
        "10*x0 + 22*x1 + 4*x2 <= 130",
        "10*x0 + 4*x2 <= 130",
        "5*x0 + 17*x1 + 2*x2 <= 73",
        "7*x0 + 20*x1 + 5*x2 <= 183",
        "7*x0 + 20*x1 + 5*x2 <= 151",
        "8*x0 + 4*x1 + 17*x2 <= 106",
        "8*x0 + 4*x1 <= 51",
        "4*x1 + 17*x2 <= 106",
        "5*x0 + 17*x1 + 2*x2 <= 73"
    ]
}
```