## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$ for milligrams of vitamin B4
- $x_1$ for milligrams of potassium
- $x_2$ for milligrams of vitamin B1
- $x_3$ for milligrams of vitamin B6

The objective function to maximize is: $2x_0 + 5x_1 + 8x_2 + 9x_3$

## Step 2: List the constraints
The constraints given are:
- $6.49x_0 \leq 288$
- $2.09x_0 \leq 351$
- $15.8x_1 \leq 288$
- $0.7x_1 \leq 351$
- $13.81x_2 \leq 288$
- $4.16x_2 \leq 351$
- $7.23x_3 \leq 288$
- $9.55x_3 \leq 351$
- $6.49x_0 + 7.23x_3 \geq 49$
- $2.09x_0 + 9.55x_3 \geq 34$
- $15.8x_1 + 13.81x_2 \leq 259$
- $15.8x_1 + 13.81x_2 + 7.23x_3 \leq 142$
- $6.49x_0 + 15.8x_1 + 13.81x_2 \leq 156$
- $6.49x_0 + 15.8x_1 + 7.23x_3 \leq 148$
- $6.49x_0 + 15.8x_1 + 13.81x_2 + 7.23x_3 \leq 148$
- $4.16x_2 + 9.55x_3 \leq 295$
- $0.7x_1 + 4.16x_2 \leq 178$
- $0.7x_1 + 4.16x_2 + 9.55x_3 \leq 113$
- $2.09x_0 + 0.7x_1 + 4.16x_2 \leq 96$
- $2.09x_0 + 0.7x_1 + 4.16x_2 + 9.55x_3 \leq 96$

## 3: Convert the problem into a Gurobi code
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", lb=0)  # milligrams of vitamin B4
x1 = m.addVar(name="x1", lb=0)  # milligrams of potassium
x2 = m.addVar(name="x2", lb=0)  # milligrams of vitamin B1
x3 = m.addVar(name="x3", lb=0)  # milligrams of vitamin B6

# Define the objective function
m.setObjective(2*x0 + 5*x1 + 8*x2 + 9*x3, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(6.49*x0 <= 288)
m.addConstr(2.09*x0 <= 351)
m.addConstr(15.8*x1 <= 288)
m.addConstr(0.7*x1 <= 351)
m.addConstr(13.81*x2 <= 288)
m.addConstr(4.16*x2 <= 351)
m.addConstr(7.23*x3 <= 288)
m.addConstr(9.55*x3 <= 351)
m.addConstr(6.49*x0 + 7.23*x3 >= 49)
m.addConstr(2.09*x0 + 9.55*x3 >= 34)
m.addConstr(15.8*x1 + 13.81*x2 <= 259)
m.addConstr(15.8*x1 + 13.81*x2 + 7.23*x3 <= 142)
m.addConstr(6.49*x0 + 15.8*x1 + 13.81*x2 <= 156)
m.addConstr(6.49*x0 + 15.8*x1 + 7.23*x3 <= 148)
m.addConstr(6.49*x0 + 15.8*x1 + 13.81*x2 + 7.23*x3 <= 148)
m.addConstr(4.16*x2 + 9.55*x3 <= 295)
m.addConstr(0.7*x1 + 4.16*x2 <= 178)
m.addConstr(0.7*x1 + 4.16*x2 + 9.55*x3 <= 113)
m.addConstr(2.09*x0 + 0.7*x1 + 4.16*x2 <= 96)
m.addConstr(2.09*x0 + 0.7*x1 + 4.16*x2 + 9.55*x3 <= 96)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of vitamin B4: {x0.varValue}")
    print(f"Milligrams of potassium: {x1.varValue}")
    print(f"Milligrams of vitamin B1: {x2.varValue}")
    print(f"Milligrams of vitamin B6: {x3.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```

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

```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B4'),
        ('x1', 'milligrams of potassium'),
        ('x2', 'milligrams of vitamin B1'),
        ('x3', 'milligrams of vitamin B6')
    ],
    'objective_function': '2*x0 + 5*x1 + 8*x2 + 9*x3',
    'constraints': [
        '6.49*x0 <= 288',
        '2.09*x0 <= 351',
        '15.8*x1 <= 288',
        '0.7*x1 <= 351',
        '13.81*x2 <= 288',
        '4.16*x2 <= 351',
        '7.23*x3 <= 288',
        '9.55*x3 <= 351',
        '6.49*x0 + 7.23*x3 >= 49',
        '2.09*x0 + 9.55*x3 >= 34',
        '15.8*x1 + 13.81*x2 <= 259',
        '15.8*x1 + 13.81*x2 + 7.23*x3 <= 142',
        '6.49*x0 + 15.8*x1 + 13.81*x2 <= 156',
        '6.49*x0 + 15.8*x1 + 7.23*x3 <= 148',
        '6.49*x0 + 15.8*x1 + 13.81*x2 + 7.23*x3 <= 148',
        '4.16*x2 + 9.55*x3 <= 295',
        '0.7*x1 + 4.16*x2 <= 178',
        '0.7*x1 + 4.16*x2 + 9.55*x3 <= 113',
        '2.09*x0 + 0.7*x1 + 4.16*x2 <= 96',
        '2.09*x0 + 0.7*x1 + 4.16*x2 + 9.55*x3 <= 96'
    ]
}
```