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

## Step 2: Convert the natural language objects into symbolic variables
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin A'
- $x_2$ for 'milligrams of vitamin B12'
- $x_3$ for 'milligrams of calcium'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $5.95x_1 + 6.95x_2 + 1.06x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $6x_1 \leq 49$
2. $3x_2 \leq 49$
3. $2x_3 \leq 49$
4. $6x_1 + 2x_3 \geq 8$
5. $6x_1 + 3x_2 \geq 16$
6. $6x_1 + 3x_2 + 2x_3 \geq 16$
7. $-5x_1 + 9x_3 \geq 0$
8. $6x_1 + 3x_2 + 2x_3 \leq 31$
9. $x_1$ is an integer
10. $x_2$ is an integer
11. $x_3$ is a real number

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin A'), ('x2', 'milligrams of vitamin B12'), ('x3', 'milligrams of calcium')],
'objective_function': '5.95*x1 + 6.95*x2 + 1.06*x3',
'constraints': [
    '6*x1 <= 49',
    '3*x2 <= 49',
    '2*x3 <= 49',
    '6*x1 + 2*x3 >= 8',
    '6*x1 + 3*x2 >= 16',
    '6*x1 + 3*x2 + 2*x3 >= 16',
    '-5*x1 + 9*x3 >= 0',
    '6*x1 + 3*x2 + 2*x3 <= 31',
    'x1 is an integer',
    'x2 is an integer'
]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='milligrams_of_vitamin_A', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='milligrams_of_vitamin_B12', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='milligrams_of_calcium')

    # Set the objective function
    model.setObjective(5.95 * x1 + 6.95 * x2 + 1.06 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(6 * x1 <= 49)
    model.addConstr(3 * x2 <= 49)
    model.addConstr(2 * x3 <= 49)
    model.addConstr(6 * x1 + 2 * x3 >= 8)
    model.addConstr(6 * x1 + 3 * x2 >= 16)
    model.addConstr(6 * x1 + 3 * x2 + 2 * x3 >= 16)
    model.addConstr(-5 * x1 + 9 * x3 >= 0)
    model.addConstr(6 * x1 + 3 * x2 + 2 * x3 <= 31)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'milligrams of vitamin A: {x1.varValue}')
        print(f'milligrams of vitamin B12: {x2.varValue}')
        print(f'milligrams of calcium: {x3.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```