To solve this optimization problem and translate it into Gurobi code, we need to break down the natural language description into a symbolic representation. This involves identifying variables, the objective function, and constraints.

### Symbolic Representation of the Problem:

Let's denote:
- `x1` as the quantity of bowls of instant ramen,
- `x2` as the quantity of corn cobs,
- `x3` as the quantity of bowls of cereal.

The **objective function** to minimize is: `2.11*x1 + 9.07*x2 + 3.76*x3`.

The **constraints** are:
1. Calcium from instant ramen and cereal: `16*x1 + 28*x3 >= 73`.
2. Calcium from corn cobs and cereal: `30*x2 + 28*x3 >= 30`.
3. Calcium from all sources (considering the specific combination mentioned): Since it's not entirely clear which combinations are meant to be considered together, we'll interpret "from bowls of instant ramen, corn cobs, and bowls of cereal" as needing at least 30 milligrams of calcium collectively from any combination that includes these items. However, given the constraints provided, this seems redundant with other requirements but we will include a constraint for the total calcium intake: `16*x1 + 30*x2 + 28*x3 >= 30`.
4. Additional constraint on instant ramen and cereal quantities: `-9*x1 + 6*x3 >= 0`.
5. Constraint on instant ramen and corn cobs quantities: `-2*x1 + 8*x2 >= 0`.
6. Total calcium intake limit: `16*x1 + 30*x2 + 28*x3 <= 113`.

### Symbolic Representation in JSON Format:

```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'
    ]
}
```

### Gurobi Code:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="bowls_of_instant_ramenn")
x2 = m.addVar(vtype=GRB.INTEGER, name="corn_cobs")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_cereal")

# Set objective function
m.setObjective(2.11*x1 + 9.07*x2 + 3.76*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(16*x1 + 28*x3 >= 73, name="calcium_from_instant_ramenn_and_cereal")
m.addConstr(30*x2 + 28*x3 >= 30, name="calcium_from_corn_cobs_and_cereal")
m.addConstr(16*x1 + 30*x2 + 28*x3 >= 30, name="total_calcium_minimum")
m.addConstr(-9*x1 + 6*x3 >= 0, name="instant_ramenn_and_cereal_quantity_constraint")
m.addConstr(-2*x1 + 8*x2 >= 0, name="instant_ramenn_and_corn_cobs_quantity_constraint")
m.addConstr(16*x1 + 30*x2 + 28*x3 <= 113, name="total_calcium_maximum")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"bowls_of_instant_ramenn: {x1.x}")
    print(f"corn_cobs: {x2.x}")
    print(f"bowls_of_cereal: {x3.x}")
else:
    print("No optimal solution found")
```