## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B9', 'milligrams of vitamin B12', 'milligrams of zinc', 'milligrams of vitamin C']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B9
- $x_1$ : milligrams of vitamin B12
- $x_2$ : milligrams of zinc
- $x_3$ : milligrams of vitamin C

## 3: Define the objective function in symbolic notation
The objective function to minimize is:
$4.34x_0^2 + 7.52x_0x_1 + 7.76x_0x_2 + 2.03x_0x_3 + 8.76x_1^2 + 4.31x_1x_3 + 3.41x_2^2 + 5.53x_2x_3 + 8.67x_1 + 7.33x_2 + 1.46x_3$

## 4: List the constraints in symbolic notation
The constraints are:
- $3.64x_0 = 3.64$
- $4.18x_0 = 4.18$
- $4.21x_1 = 4.21$
- $6.2x_1 = 6.2$
- $2.45x_2 = 2.45$
- $8.81x_2 = 8.81$
- $4.62x_3 = 4.62$
- $6.88x_3 = 6.88$
- $4.21x_1 + 2.45x_2 \geq 59$
- $4.21x_1 + 4.62x_3 \geq 28$
- $3.64x_0 + 2.45x_2 \geq 21$
- $3.64x_0 + 4.62x_3 \geq 25$
- $(3.64x_0)^2 + (4.21x_1)^2 \geq 39$
- $4.21x_1 + 2.45x_2 + 4.62x_3 \geq 59$
- $3.64x_0 + 4.21x_1 + 2.45x_2 + 4.62x_3 \geq 59$
- $4.18x_0 + 6.88x_3 \geq 29$
- $4.18x_0 + 6.2x_1 \geq 20$
- $4.18x_0 + 8.81x_2 \geq 10$
- $(6.2x_1)^2 + (6.88x_3)^2 \geq 18$
- $4.18x_0 + 6.2x_1 + 8.81x_2 + 6.88x_3 \geq 18$
- $3(3.64x_0)^2 - 2(4.62x_3)^2 \geq 0$
- $3.64x_0 + 4.62x_3 \leq 128$
- $4.21x_1 + 2.45x_2 + 4.62x_3 \leq 128$

## 5: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 6: Implement the objective function and constraints in Gurobi
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="x0", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x1 = m.addVar(name="x1", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="x2", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x3 = m.addVar(name="x3", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

# Objective function
m.setObjective(4.34*x0**2 + 7.52*x0*x1 + 7.76*x0*x2 + 2.03*x0*x3 + 
               8.76*x1**2 + 4.31*x1*x3 + 3.41*x2**2 + 5.53*x2*x3 + 
               8.67*x1 + 7.33*x2 + 1.46*x3, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(3.64*x0 == 3.64, name="c1")
m.addConstr(4.18*x0 == 4.18, name="c2")
m.addConstr(4.21*x1 == 4.21, name="c3")
m.addConstr(6.2*x1 == 6.2, name="c4")
m.addConstr(2.45*x2 == 2.45, name="c5")
m.addConstr(8.81*x2 == 8.81, name="c6")
m.addConstr(4.62*x3 == 4.62, name="c7")
m.addConstr(6.88*x3 == 6.88, name="c8")
m.addConstr(4.21*x1 + 2.45*x2 >= 59, name="c9")
m.addConstr(4.21*x1 + 4.62*x3 >= 28, name="c10")
m.addConstr(3.64*x0 + 2.45*x2 >= 21, name="c11")
m.addConstr(3.64*x0 + 4.62*x3 >= 25, name="c12")
m.addConstr((3.64*x0)**2 + (4.21*x1)**2 >= 39, name="c13")
m.addConstr(4.21*x1 + 2.45*x2 + 4.62*x3 >= 59, name="c14")
m.addConstr(3.64*x0 + 4.21*x1 + 2.45*x2 + 4.62*x3 >= 59, name="c15")
m.addConstr(4.18*x0 + 6.88*x3 >= 29, name="c16")
m.addConstr(4.18*x0 + 6.2*x1 >= 20, name="c17")
m.addConstr(4.18*x0 + 8.81*x2 >= 10, name="c18")
m.addConstr((6.2*x1)**2 + (6.88*x3)**2 >= 18, name="c19")
m.addConstr(4.18*x0 + 6.2*x1 + 8.81*x2 + 6.88*x3 >= 18, name="c20")
m.addConstr(3*(3.64*x0)**2 - 2*(4.62*x3)**2 >= 0, name="c21")
m.addConstr(3.64*x0 + 4.62*x3 <= 128, name="c22")
m.addConstr(4.21*x1 + 2.45*x2 + 4.62*x3 <= 128, name="c23")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", m.objval)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
else:
    print("The model is infeasible")
```

## Step 7: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B9'), 
        ('x1', 'milligrams of vitamin B12'), 
        ('x2', 'milligrams of zinc'), 
        ('x3', 'milligrams of vitamin C')
    ], 
    'objective_function': '4.34*x0^2 + 7.52*x0*x1 + 7.76*x0*x2 + 2.03*x0*x3 + 8.76*x1^2 + 4.31*x1*x3 + 3.41*x2^2 + 5.53*x2*x3 + 8.67*x1 + 7.33*x2 + 1.46*x3', 
    'constraints': [
        '3.64*x0 == 3.64', 
        '4.18*x0 == 4.18', 
        '4.21*x1 == 4.21', 
        '6.2*x1 == 6.2', 
        '2.45*x2 == 2.45', 
        '8.81*x2 == 8.81', 
        '4.62*x3 == 4.62', 
        '6.88*x3 == 6.88', 
        '4.21*x1 + 2.45*x2 >= 59', 
        '4.21*x1 + 4.62*x3 >= 28', 
        '3.64*x0 + 2.45*x2 >= 21', 
        '3.64*x0 + 4.62*x3 >= 25', 
        '(3.64*x0)^2 + (4.21*x1)^2 >= 39', 
        '4.21*x1 + 2.45*x2 + 4.62*x3 >= 59', 
        '3.64*x0 + 4.21*x1 + 2.45*x2 + 4.62*x3 >= 59', 
        '4.18*x0 + 6.88*x3 >= 29', 
        '4.18*x0 + 6.2*x1 >= 20', 
        '4.18*x0 + 8.81*x2 >= 10', 
        '(6.2*x1)^2 + (6.88*x3)^2 >= 18', 
        '4.18*x0 + 6.2*x1 + 8.81*x2 + 6.88*x3 >= 18', 
        '3*(3.64*x0)^2 - 2*(4.62*x3)^2 >= 0', 
        '3.64*x0 + 4.62*x3 <= 128', 
        '4.21*x1 + 2.45*x2 + 4.62*x3 <= 128'
    ]
}
```