## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B9', 'milligrams of magnesium', and 'milligrams of calcium', which can be represented symbolically 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 and their corresponding natural language objects are:
- $x_1$ : milligrams of vitamin B9
- $x_2$ : milligrams of magnesium
- $x_3$ : milligrams of calcium

## 3: Define the objective function in symbolic notation
The objective function to maximize is $2.05x_1 + 9.06x_2 + 5.68x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $6.95x_1 \leq 129$
- $15.5x_1 \leq 311$
- $9.98x_1 \leq 190$
- $2.8x_2 \leq 129$
- $7.18x_2 \leq 311$
- $7.57x_2 \leq 190$
- $5.16x_3 \leq 129$
- $9.45x_3 \leq 311$
- $2.62x_3 \leq 190$
- $6.95x_1 + 2.8x_2 + 5.16x_3 \geq 23$
- $9.98x_1 + 7.57x_2 + 2.62x_3 \geq 42$
- $6.95x_1 + 2.8x_2 \leq 77$
- $2.8x_2 + 5.16x_3 \leq 48$
- $6.95x_1 + 2.8x_2 + 5.16x_3 \leq 117$
- $6.95x_1 + 2.8x_2 + 5.16x_3 \leq 117$
- $7.18x_2 + 9.45x_3 \leq 112$
- $15.5x_1 + 7.18x_2 \leq 267$
- $15.5x_1 + 7.18x_2 + 9.45x_3 \leq 310$
- $15.5x_1 + 7.18x_2 + 9.45x_3 \leq 310$
- $9.98x_1 + 2.62x_3 \leq 85$
- $7.57x_2 + 2.62x_3 \leq 116$
- $9.98x_1 + 7.57x_2 + 2.62x_3 \leq 116$

## 5: Create the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B9'), 
        ('x2', 'milligrams of magnesium'), 
        ('x3', 'milligrams of calcium')
    ], 
    'objective_function': '2.05x1 + 9.06x2 + 5.68x3', 
    'constraints': [
        '6.95x1 <= 129', 
        '15.5x1 <= 311', 
        '9.98x1 <= 190', 
        '2.8x2 <= 129', 
        '7.18x2 <= 311', 
        '7.57x2 <= 190', 
        '5.16x3 <= 129', 
        '9.45x3 <= 311', 
        '2.62x3 <= 190', 
        '6.95x1 + 2.8x2 + 5.16x3 >= 23', 
        '9.98x1 + 7.57x2 + 2.62x3 >= 42', 
        '6.95x1 + 2.8x2 <= 77', 
        '2.8x2 + 5.16x3 <= 48', 
        '6.95x1 + 2.8x2 + 5.16x3 <= 117', 
        '7.18x2 + 9.45x3 <= 112', 
        '15.5x1 + 7.18x2 <= 267', 
        '15.5x1 + 7.18x2 + 9.45x3 <= 310', 
        '9.98x1 + 2.62x3 <= 85', 
        '7.57x2 + 2.62x3 <= 116', 
        '9.98x1 + 7.57x2 + 2.62x3 <= 116'
    ]
}
```

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

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

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B9
    x2 = model.addVar(name="x2", lb=0)  # milligrams of magnesium
    x3 = model.addVar(name="x3", lb=0)  # milligrams of calcium

    # Objective function
    model.setObjective(2.05 * x1 + 9.06 * x2 + 5.68 * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6.95 * x1 <= 129)
    model.addConstr(15.5 * x1 <= 311)
    model.addConstr(9.98 * x1 <= 190)
    model.addConstr(2.8 * x2 <= 129)
    model.addConstr(7.18 * x2 <= 311)
    model.addConstr(7.57 * x2 <= 190)
    model.addConstr(5.16 * x3 <= 129)
    model.addConstr(9.45 * x3 <= 311)
    model.addConstr(2.62 * x3 <= 190)
    model.addConstr(6.95 * x1 + 2.8 * x2 + 5.16 * x3 >= 23)
    model.addConstr(9.98 * x1 + 7.57 * x2 + 2.62 * x3 >= 42)
    model.addConstr(6.95 * x1 + 2.8 * x2 <= 77)
    model.addConstr(2.8 * x2 + 5.16 * x3 <= 48)
    model.addConstr(6.95 * x1 + 2.8 * x2 + 5.16 * x3 <= 117)
    model.addConstr(7.18 * x2 + 9.45 * x3 <= 112)
    model.addConstr(15.5 * x1 + 7.18 * x2 <= 267)
    model.addConstr(15.5 * x1 + 7.18 * x2 + 9.45 * x3 <= 310)
    model.addConstr(9.98 * x1 + 2.62 * x3 <= 85)
    model.addConstr(7.57 * x2 + 2.62 * x3 <= 116)
    model.addConstr(9.98 * x1 + 7.57 * x2 + 2.62 * x3 <= 116)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B9: {x1.varValue}")
        print(f"Milligrams of magnesium: {x2.varValue}")
        print(f"Milligrams of calcium: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```