## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B5', 'milligrams of vitamin B12', 'milligrams of vitamin B9', 'milligrams of zinc']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin B5
- $x_2$ : milligrams of vitamin B12
- $x_3$ : milligrams of vitamin B9
- $x_4$ : milligrams of zinc

## 3: Define the objective function in symbolic notation
The objective function to maximize is:
$2.62x_1^2 + 7.0x_1x_2 + 1.25x_1x_3 + 5.29x_2^2 + 1.87x_2x_3 + 9.68x_2x_4 + 9.91x_3x_4 + 9.91x_4^2 + 5.78x_1 + 5.09x_2 + 2.98x_3 + 9.3x_4$

## 4: List the constraints in symbolic notation
The constraints are:
- $10x_1 \leq 128$
- $7x_1 \leq 201$
- $10x_1 \leq 202$
- $14x_1 \leq 138$
- $3x_2 \leq 128$
- $13x_2 \leq 201$
- $2x_2 \leq 202$
- $4x_2 \leq 138$
- $6x_3 \leq 128$
- $13x_3 \leq 201$
- $17x_3 \leq 202$
- $11x_3 \leq 138$
- $3x_4 \leq 128$
- $17x_4 \leq 201$
- $15x_4 \leq 202$
- $7x_4 \leq 138$
- $10x_1 + 3x_2 \geq 15$
- $6x_3 + 3x_4 \geq 16$
- $3x_2 + 3x_4 \geq 15$
- $10x_1^2 + 6x_3^2 \geq 29$
- $10x_1^2 + 3x_2^2 + 6x_3^2 \geq 26$
- $7x_1 + 13x_3 + 17x_4 \geq 30$
- $4x_2 + 7x_4 \geq 11$
- $14x_1^2 + 11x_3^2 \geq 33$
- $14x_1 + 7x_4 \geq 23$
- $14x_1 + 11x_3 + 7x_4 \geq 26$
- $10x_1 + 3x_4 \leq 105$
- $10x_1^2 + 6x_3^2 \leq 92$
- $10x_1^2 + 3x_2^2 + x_4^2 \leq 104$
- $10x_1 + 3x_2 + 6x_3 + 3x_4 \leq 104$
- $7x_1^2 + 17x_4^2 \leq 67$
- $13x_2^2 + 13x_3^2 \leq 173$
- $7x_1 + 13x_3 \leq 179$
- $13x_2 + 17x_4 \leq 141$
- $13x_3 + 17x_4 \leq 96$
- $7x_1 + 13x_2 + 17x_4 \leq 128$
- $7x_1^2 + 13x_2^2 + 13x_3^2 \leq 51$
- $7x_1 + 13x_2 + 13x_3 + 17x_4 \leq 51$
- $2x_2 + 17x_3 \leq 119$
- $10x_1 + 2x_2 \leq 52$
- $10x_1 + 2x_2 + 17x_3 + 15x_4 \leq 52$
- $4x_2 + 11x_3 \leq 90$
- $14x_1^2 + 4x_2^2 \leq 71$
- $11x_3^2 + 7x_4^2 \leq 37$
- $14x_1 + 4x_2 + 11x_3 + 7x_4 \leq 37$

## 5: Determine the variable types
- $x_1$ : Continuous
- $x_2$ : Integer
- $x_3$ : Integer
- $x_4$ : Continuous

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

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

# Define the variables
x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1", vtype=gurobi.GRB.CONTINUOUS)
x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2", vtype=gurobi.GRB.INTEGER)
x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x3", vtype=gurobi.GRB.INTEGER)
x4 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x4", vtype=gurobi.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(2.62*x1**2 + 7.0*x1*x2 + 1.25*x1*x3 + 5.29*x2**2 + 1.87*x2*x3 + 9.68*x2*x4 + 9.91*x3*x4 + 9.91*x4**2 + 5.78*x1 + 5.09*x2 + 2.98*x3 + 9.3*x4, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(10*x1 <= 128)
model.addConstr(7*x1 <= 201)
model.addConstr(10*x1 <= 202)
model.addConstr(14*x1 <= 138)
model.addConstr(3*x2 <= 128)
model.addConstr(13*x2 <= 201)
model.addConstr(2*x2 <= 202)
model.addConstr(4*x2 <= 138)
model.addConstr(6*x3 <= 128)
model.addConstr(13*x3 <= 201)
model.addConstr(17*x3 <= 202)
model.addConstr(11*x3 <= 138)
model.addConstr(3*x4 <= 128)
model.addConstr(17*x4 <= 201)
model.addConstr(15*x4 <= 202)
model.addConstr(7*x4 <= 138)
model.addConstr(10*x1 + 3*x2 >= 15)
model.addConstr(6*x3 + 3*x4 >= 16)
model.addConstr(3*x2 + 3*x4 >= 15)
model.addConstr(10*x1**2 + 6*x3**2 >= 29)
model.addConstr(10*x1**2 + 3*x2**2 + 6*x3**2 >= 26)
model.addConstr(7*x1 + 13*x3 + 17*x4 >= 30)
model.addConstr(4*x2 + 7*x4 >= 11)
model.addConstr(14*x1**2 + 11*x3**2 >= 33)
model.addConstr(14*x1 + 7*x4 >= 23)
model.addConstr(14*x1 + 11*x3 + 7*x4 >= 26)
model.addConstr(10*x1 + 3*x4 <= 105)
model.addConstr(10*x1**2 + 6*x3**2 <= 92)
model.addConstr(10*x1**2 + 3*x2**2 + x4**2 <= 104)
model.addConstr(10*x1 + 3*x2 + 6*x3 + 3*x4 <= 104)
model.addConstr(7*x1**2 + 17*x4**2 <= 67)
model.addConstr(13*x2**2 + 13*x3**2 <= 173)
model.addConstr(7*x1 + 13*x3 <= 179)
model.addConstr(13*x2 + 17*x4 <= 141)
model.addConstr(13*x3 + 17*x4 <= 96)
model.addConstr(7*x1 + 13*x2 + 17*x4 <= 128)
model.addConstr(7*x1**2 + 13*x2**2 + 13*x3**2 <= 51)
model.addConstr(7*x1 + 13*x2 + 13*x3 + 17*x4 <= 51)
model.addConstr(2*x2 + 17*x3 <= 119)
model.addConstr(10*x1 + 2*x2 <= 52)
model.addConstr(10*x1 + 2*x2 + 17*x3 + 15*x4 <= 52)
model.addConstr(4*x2 + 11*x3 <= 90)
model.addConstr(14*x1**2 + 4*x2**2 <= 71)
model.addConstr(11*x3**2 + 7*x4**2 <= 37)
model.addConstr(14*x1 + 4*x2 + 11*x3 + 7*x4 <= 37)

# Optimize the model
model.optimize()

# Print the solution
print("Objective: ", model.objVal)
print("x1: ", x1.varValue)
print("x2: ", x2.varValue)
print("x3: ", x3.varValue)
print("x4: ", x4.varValue)

```
## Step 7: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B5'), 
        ('x2', 'milligrams of vitamin B12'), 
        ('x3', 'milligrams of vitamin B9'), 
        ('x4', 'milligrams of zinc')
    ], 
    'objective_function': '2.62*x1^2 + 7.0*x1*x2 + 1.25*x1*x3 + 5.29*x2^2 + 1.87*x2*x3 + 9.68*x2*x4 + 9.91*x3*x4 + 9.91*x4^2 + 5.78*x1 + 5.09*x2 + 2.98*x3 + 9.3*x4', 
    'constraints': [
        '10*x1 <= 128', 
        '7*x1 <= 201', 
        '10*x1 <= 202', 
        '14*x1 <= 138', 
        '3*x2 <= 128', 
        '13*x2 <= 201', 
        '2*x2 <= 202', 
        '4*x2 <= 138', 
        '6*x3 <= 128', 
        '13*x3 <= 201', 
        '17*x3 <= 202', 
        '11*x3 <= 138', 
        '3*x4 <= 128', 
        '17*x4 <= 201', 
        '15*x4 <= 202', 
        '7*x4 <= 138', 
        '10*x1 + 3*x2 >= 15', 
        '6*x3 + 3*x4 >= 16', 
        '3*x2 + 3*x4 >= 15', 
        '10*x1^2 + 6*x3^2 >= 29', 
        '10*x1^2 + 3*x2^2 + 6*x3^2 >= 26', 
        '7*x1 + 13*x3 + 17*x4 >= 30', 
        '4*x2 + 7*x4 >= 11', 
        '14*x1^2 + 11*x3^2 >= 33', 
        '14*x1 + 7*x4 >= 23', 
        '14*x1 + 11*x3 + 7*x4 >= 26', 
        '10*x1 + 3*x4 <= 105', 
        '10*x1^2 + 6*x3^2 <= 92', 
        '10*x1^2 + 3*x2^2 + x4^2 <= 104', 
        '10*x1 + 3*x2 + 6*x3 + 3*x4 <= 104', 
        '7*x1^2 + 17*x4^2 <= 67', 
        '13*x2^2 + 13*x3^2 <= 173', 
        '7*x1 + 13*x3 <= 179', 
        '13*x2 + 17*x4 <= 141', 
        '13*x3 + 17*x4 <= 96', 
        '7*x1 + 13*x2 + 17*x4 <= 128', 
        '7*x1^2 + 13*x2^2 + 13*x3^2 <= 51', 
        '7*x1 + 13*x2 + 13*x3 + 17*x4 <= 51', 
        '2*x2 + 17*x3 <= 119', 
        '10*x1 + 2*x2 <= 52', 
        '10*x1 + 2*x2 + 17*x3 + 15*x4 <= 52', 
        '4*x2 + 11*x3 <= 90', 
        '14*x1^2 + 4*x2^2 <= 71', 
        '11*x3^2 + 7*x4^2 <= 37', 
        '14*x1 + 4*x2 + 11*x3 + 7*x4 <= 37'
    ]
}
```