## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of instant ramen', 'corn cobs', and 'bowls of cereal', which we can denote as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2.11x_1 + 9.07x_2 + 3.76x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $16x_1 + 30x_2 + 28x_3 \leq 239$ (calcium upper bound, but we need to consider the specific calcium constraints),
2. $16x_1 + 28x_3 \geq 73$ (at least 73 milligrams of calcium from bowls of instant ramen and bowls of cereal),
3. $30x_2 + 28x_3 \geq 30$ (at least 30 milligrams of calcium from corn cobs and bowls of cereal),
4. $16x_1 + 30x_2 + 28x_3 \geq 30$ (at least 30 milligrams of calcium from all sources),
5. $-9x_1 + 6x_3 \geq 0$ (linear constraint),
6. $-2x_1 + 8x_2 \geq 0$ (linear constraint),
7. $16x_1 + 30x_2 + 28x_3 \leq 113$ (no more than 113 milligrams of calcium from all sources).

## 4: Consider variable bounds and types
- $x_1$ is an integer (bowls of instant ramen),
- $x_2$ is an integer (corn cobs),
- $x_3$ can be fractional (bowls of cereal).

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'bowls of instant ramen'), ('x2', 'corn cobs'), ('x3', 'bowls of cereal')],
'objective_function': '2.11*x1 + 9.07*x2 + 3.76*x3',
'constraints': [
    '16*x1 + 28*x3 >= 73',
    '30*x2 + 28*x3 >= 30',
    '16*x1 + 30*x2 + 28*x3 >= 30',
    '-9*x1 + 6*x3 >= 0',
    '-2*x1 + 8*x2 >= 0',
    '16*x1 + 30*x2 + 28*x3 <= 113',
    '16*x1 + 30*x2 + 28*x3 <= 239'
]
}
```

## 6: Gurobi Code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="bowls_of_instant_ramen", vtype=gurobi.GRB.INTEGER)  # bowls of instant ramen
    x2 = model.addVar(name="corn_cobs", vtype=gurobi.GRB.INTEGER)  # corn cobs
    x3 = model.addVar(name="bowls_of_cereal")  # bowls of cereal

    # Objective function
    model.setObjective(2.11 * x1 + 9.07 * x2 + 3.76 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(16 * x1 + 28 * x3 >= 73, name="calcium_from_ramen_and_cereal")
    model.addConstr(30 * x2 + 28 * x3 >= 30, name="calcium_from_corn_cobs_and_cereal")
    model.addConstr(16 * x1 + 30 * x2 + 28 * x3 >= 30, name="calcium_from_all_sources_min")
    model.addConstr(-9 * x1 + 6 * x3 >= 0, name="linear_constraint_1")
    model.addConstr(-2 * x1 + 8 * x2 >= 0, name="linear_constraint_2")
    model.addConstr(16 * x1 + 30 * x2 + 28 * x3 <= 113, name="calcium_from_all_sources_max")
    model.addConstr(16 * x1 + 30 * x2 + 28 * x3 <= 239, name="calcium_upper_bound")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("bowls of instant ramen: ", x1.varValue)
        print("corn cobs: ", x2.varValue)
        print("bowls of cereal: ", x3.varValue)
    else:
        print("No solution found")

solve_optimization_problem()
```