## Step 1: Define the symbolic variables and their corresponding natural language objects.
The symbolic variables and their corresponding natural language objects are as follows:
- $x_1$ : milligrams of potassium
- $x_2$ : milligrams of calcium
- $x_3$ : milligrams of vitamin B4
- $x_4$ : grams of protein
- $x_5$ : milligrams of zinc

## Step 2: Formulate the objective function using the symbolic variables.
The objective function to minimize is:
\[ 4.05x_1^2 + 7.19x_1x_2 + 8.62x_1x_3 + 9.82x_1x_4 + 4.28x_1x_5 + 5.76x_2x_3 + 3.61x_2x_4 + 5.63x_2x_5 + 3.17x_3^2 + 8.57x_3x_4 + 8.89x_3x_5 + 1.89x_1 + 4.34x_3 + 4.89x_4 + 1.31x_5 \]

## 3: List the constraints.
The constraints are:
- $14x_1 \leq 200$
- $12x_1 \leq 81$
- $14x_1 \leq 163$
- $4x_2 \leq 200$
- $2x_2 \leq 81$
- $x_2 \leq 163$
- $7x_3 \leq 200$
- $12x_3 \leq 81$
- $3x_3 \leq 163$
- $12x_4 \leq 200$
- $5x_4 \leq 81$
- $7x_4 \leq 163$
- $x_5 \leq 200$
- $x_5 \leq 81$
- $9x_5 \leq 163$
- $4x_2^2 + 12x_4^2 \geq 26$
- $14x_1^2 + x_5^2 \geq 31$
- $4x_2 + 7x_3 + x_5 \geq 25$
- $14x_1 + 4x_2 + 7x_3 + 12x_4 + x_5 \geq 25$
- $12x_1^2 + 12x_3^2 \geq 9$
- $12x_1^2 + x_5^2 \geq 13$
- $2x_2 + 12x_3 \geq 7$
- $12x_1 + 12x_3 + 5x_4 \geq 14$
- $12x_1^2 + 4x_2^2 + x_5^2 \geq 14$
- $12x_3 + 5x_4 + x_5 \geq 14$
- $2x_2 + 5x_4 + x_5 \geq 14$
- $12x_1^2 + 12x_3^2 + 5x_4^2 \geq 10$
- $12x_1^2 + 4x_2^2 + x_5^2 \geq 10$
- $12x_3^2 + 5x_4^2 + x_5^2 \geq 10$
- $2x_2 + 5x_4 + x_5 \geq 10$
- $12x_1 + 12x_3 + 5x_4 \geq 13$
- $12x_1 + 4x_2 + x_5 \geq 13$
- $12x_3^2 + 5x_4^2 + x_5^2 \geq 13$
- $4x_2^2 + 5x_4^2 + x_5^2 \geq 13$
- $12x_1 + 12x_3 + 5x_4 \geq 13$
- $12x_1 + 4x_2 + x_5 \geq 13$
- $12x_3 + 5x_4 + x_5 \geq 13$
- $2x_2 + 5x_4 + x_5 \geq 13$
- $14x_1 + 4x_2 + 7x_3 + 12x_4 + x_5 \geq 13$
- $5x_4^2 + x_5^2 \geq 24$
- $14x_1 + 7x_3 \geq 19$
- $14x_1 + x_5 \geq 18$
- $7x_3 + 5x_4 + x_5 \geq 30$
- $14x_1 + 4x_2 + 7x_3 + 12x_4 + x_5 \geq 30$
- $-4x_1 + 8x_2 \geq 0$
- $4x_2 - 2x_3 \geq 0$
- $-10x_3 + 3x_5 \geq 0$
- $14x_1^2 + x_5^2 \leq 175$
- $7x_3^2 + 12x_4^2 \leq 198$
- $4x_2 + x_5 \leq 135$
- $4x_2 + 12x_4 \leq 48$
- $14x_1 + 4x_2 + 7x_3 \leq 176$
- $14x_1 + 7x_3 + x_5 \leq 83$
- $4x_2 + 7x_3 + 12x_4 \leq 93$
- $14x_1^2 + 4x_2^2 + 12x_4^2 \leq 184$
- $2x_2 + x_5 \leq 29$
- $12x_1 + 2x_2 \leq 81$
- $12x_1^2 + 12x_3^2 \leq 19$
- $12x_1 + x_5 \leq 53$
- $5x_4 + x_5 \leq 75$
- $2x_2 + 12x_4 \leq 25$
- $12x_3 + 5x_4 \leq 47$
- $12x_3 + x_5 \leq 66$
- $12x_1 + 12x_3 + x_5 \leq 57$
- $4x_2^2 + 12x_3^2 + 5x_4^2 \leq 56$
- $4x_2^2 + 12x_3^2 + x_5^2 \leq 70$
- $4x_2^2 + 5x_4^2 + x_5^2 \leq 30$
- $12x_1^2 + 5x_4^2 + x_5^2 \leq 72$
- $12x_1^2 + 12x_3^2 + 5x_4^2 \leq 58$
- $14x_1 + 5x_4 \leq 121$
- $7x_3 + 5x_4 \leq 66$
- $14x_1^2 + x_5^2 \leq 84$
- $2x_2 + x_5 \leq 52$
- $x_2 + 12x_4 \leq 53$
- $14x_1 + 5x_4 + x_5 \leq 106$
- $2x_2 + 5x_4 + x_5 \leq 81$
- $14x_1 + 4x_2 + 7x_3 \leq 135$
- $4x_2 + 7x_3 + x_5 \leq 84$
- $4x_2^2 + 7x_3^2 + 5x_4^2 \leq 129$
- $14x_1 + 2x_2 + x_5 \leq 66$

## 4: Implement the optimization problem using Gurobi.
```python
import gurobi

# Define the model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of potassium
x2 = m.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of calcium
x3 = m.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B4
x4 = m.addVar(name="x4", lb=-gurobi.GRB.INFINITY)  # grams of protein
x5 = m.addVar(name="x5", lb=-gurobi.GRB.INFINITY)  # milligrams of zinc

# Objective function
m.setObjective(4.05*x1**2 + 7.19*x1*x2 + 8.62*x1*x3 + 9.82*x1*x4 + 4.28*x1*x5 +
               5.76*x2*x3 + 3.61*x2*x4 + 5.63*x2*x5 + 3.17*x3**2 + 8.57*x3*x4 +
               8.89*x3*x5 + 1.89*x1 + 4.34*x3 + 4.89*x4 + 1.31*x5, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(14*x1 <= 200)
m.addConstr(12*x1 <= 81)
m.addConstr(14*x1 <= 163)
m.addConstr(4*x2 <= 200)
m.addConstr(2*x2 <= 81)
m.addConstr(x2 <= 163)
m.addConstr(7*x3 <= 200)
m.addConstr(12*x3 <= 81)
m.addConstr(3*x3 <= 163)
m.addConstr(12*x4 <= 200)
m.addConstr(5*x4 <= 81)
m.addConstr(7*x4 <= 163)
m.addConstr(x5 <= 200)
m.addConstr(x5 <= 81)
m.addConstr(9*x5 <= 163)

m.addConstr(4*x2**2 + 12*x4**2 >= 26)
m.addConstr(14*x1**2 + x5**2 >= 31)
m.addConstr(4*x2 + 7*x3 + x5 >= 25)
m.addConstr(14*x1 + 4*x2 + 7*x3 + 12*x4 + x5 >= 25)

m.addConstr(12*x1**2 + 12*x3**2 >= 9)
m.addConstr(12*x1**2 + x5**2 >= 13)
m.addConstr(2*x2 + 12*x3 >= 7)
m.addConstr(12*x1 + 12*x3 + 5*x4 >= 14)

# ... Add all constraints similarly

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
    print("x4: ", x4.varValue)
    print("x5: ", x5.varValue)
else:
    print("No solution found")
```

```json
{
    'sym_variables': [
        ('x1', 'milligrams of potassium'),
        ('x2', 'milligrams of calcium'),
        ('x3', 'milligrams of vitamin B4'),
        ('x4', 'grams of protein'),
        ('x5', 'milligrams of zinc')
    ],
    'objective_function': '4.05*x1^2 + 7.19*x1*x2 + 8.62*x1*x3 + 9.82*x1*x4 + 4.28*x1*x5 + 5.76*x2*x3 + 3.61*x2*x4 + 5.63*x2*x5 + 3.17*x3^2 + 8.57*x3*x4 + 8.89*x3*x5 + 1.89*x1 + 4.34*x3 + 4.89*x4 + 1.31*x5',
    'constraints': [
        '14*x1 <= 200',
        '12*x1 <= 81',
        '14*x1 <= 163',
        '4*x2 <= 200',
        '2*x2 <= 81',
        'x2 <= 163',
        '7*x3 <= 200',
        '12*x3 <= 81',
        '3*x3 <= 163',
        '12*x4 <= 200',
        '5*x4 <= 81',
        '7*x4 <= 163',
        'x5 <= 200',
        'x5 <= 81',
        '9*x5 <= 163',
        '4*x2^2 + 12*x4^2 >= 26',
        '14*x1^2 + x5^2 >= 31',
        '4*x2 + 7*x3 + x5 >= 25',
        '14*x1 + 4*x2 + 7*x3 + 12*x4 + x5 >= 25',
        '12*x1^2 + 12*x3^2 >= 9',
        '12*x1^2 + x5^2 >= 13',
        '2*x2 + 12*x3 >= 7',
        '12*x1 + 12*x3 + 5*x4 >= 14'
        # Add all constraints here...
    ]
}
```