## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B2', 'milligrams of calcium', 'milligrams of vitamin D']. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ = 'milligrams of vitamin B2'
- $x_2$ = 'milligrams of calcium'
- $x_3$ = 'milligrams of vitamin D'

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $7x_1^2 + 7x_1x_2 + x_2^2 + 3x_3^2 + 6x_1$.

## 4: List the constraints in symbolic notation
The constraints are:
- $1.8x_1 \leq 164$
- $14.76x_1 \leq 300$
- $13.5x_2 \leq 164$
- $10.92x_2 \leq 300$
- $9.2x_3 \leq 164$
- $13.51x_3 \leq 300$
- $(1.8)^2x_1^2 + (9.2)^2x_3^2 \geq 32$
- $(13.5)^2x_2^2 + (9.2)^2x_3^2 \geq 54$
- $(1.8)^2x_1^2 + (13.5)^2x_2^2 \geq 19$
- $10.92x_2 + 13.51x_3 \geq 76$
- $(14.76)^2x_1^2 + (13.51)^2x_3^2 \geq 44$
- $1.8x_1 + 13.5x_2 \leq 88$
- $(1.8)^2x_1^2 + (9.2)^2x_3^2 \leq 158$
- $(1.8)^2x_1^2 + (13.5)^2x_2^2 + (9.2)^2x_3^2 \leq 111$
- $1.8x_1 + 13.5x_2 + 9.2x_3 \leq 111$
- $(14.76)^2x_1^2 + (13.51)^2x_3^2 \leq 152$
- $10.92x_2 + 13.51x_3 \leq 100$
- $14.76x_1 + 10.92x_2 + 13.51x_3 \leq 100$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x1', 'milligrams of vitamin B2'), 
    ('x2', 'milligrams of calcium'), 
    ('x3', 'milligrams of vitamin D')
],
'objective_function': '7*x1^2 + 7*x1*x2 + 1*x2^2 + 3*x3^2 + 6*x1',
'constraints': [
    '1.8*x1 <= 164',
    '14.76*x1 <= 300',
    '13.5*x2 <= 164',
    '10.92*x2 <= 300',
    '9.2*x3 <= 164',
    '13.51*x3 <= 300',
    '3.24*x1^2 + 84.64*x3^2 >= 32',
    '182.25*x2^2 + 84.64*x3^2 >= 54',
    '3.24*x1^2 + 182.25*x2^2 >= 19',
    '10.92*x2 + 13.51*x3 >= 76',
    '217.4976*x1^2 + 182.4201*x3^2 >= 44',
    '1.8*x1 + 13.5*x2 <= 88',
    '3.24*x1^2 + 84.64*x3^2 <= 158',
    '3.24*x1^2 + 182.25*x2^2 + 84.64*x3^2 <= 111',
    '1.8*x1 + 13.5*x2 + 9.2*x3 <= 111',
    '217.4976*x1^2 + 182.4201*x3^2 <= 152',
    '10.92*x2 + 13.51*x3 <= 100',
    '14.76*x1 + 10.92*x2 + 13.51*x3 <= 100'
]
}
```

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

def optimize_problem():
    model = gurobi.Model()

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

    # Objective function
    model.setObjective(7*x1**2 + 7*x1*x2 + x2**2 + 3*x3**2 + 6*x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(1.8*x1 <= 164)
    model.addConstr(14.76*x1 <= 300)
    model.addConstr(13.5*x2 <= 164)
    model.addConstr(10.92*x2 <= 300)
    model.addConstr(9.2*x3 <= 164)
    model.addConstr(13.51*x3 <= 300)
    model.addConstr(3.24*x1**2 + 84.64*x3**2 >= 32)
    model.addConstr(182.25*x2**2 + 84.64*x3**2 >= 54)
    model.addConstr(3.24*x1**2 + 182.25*x2**2 >= 19)
    model.addConstr(10.92*x2 + 13.51*x3 >= 76)
    model.addConstr(217.4976*x1**2 + 182.4201*x3**2 >= 44)
    model.addConstr(1.8*x1 + 13.5*x2 <= 88)
    model.addConstr(3.24*x1**2 + 84.64*x3**2 <= 158)
    model.addConstr(3.24*x1**2 + 182.25*x2**2 + 84.64*x3**2 <= 111)
    model.addConstr(1.8*x1 + 13.5*x2 + 9.2*x3 <= 111)
    model.addConstr(217.4976*x1**2 + 182.4201*x3**2 <= 152)
    model.addConstr(10.92*x2 + 13.51*x3 <= 100)
    model.addConstr(14.76*x1 + 10.92*x2 + 13.51*x3 <= 100)

    # Solve the model
    model.optimize()

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

optimize_problem()
```