To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the information provided.

Let's define:
- $x_1$ as the number of servings of beans eaten,
- $x_2$ as the number of servings of cereal eaten.

The objective is to minimize the total cost, which can be represented by the objective function: $2x_1 + x_2$, since each serving of beans costs $2 and each serving of cereal costs $1.

The constraints based on the requirements are:
- For carbohydrates: $50x_1 + 30x_2 \geq 300$ (to get at least 300 g of carbohydrates),
- For protein: $20x_1 + 5x_2 \geq 150$ (to get at least 150 g of protein),
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$, since the number of servings cannot be negative.

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'servings of beans'), ('x2', 'servings of cereal')],
    'objective_function': '2*x1 + x2',
    'constraints': ['50*x1 + 30*x2 >= 300', '20*x1 + 5*x2 >= 150', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_beans")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_cereal")

# Set the objective function
m.setObjective(2*x1 + x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(50*x1 + 30*x2 >= 300, "carbohydrate_requirement")
m.addConstr(20*x1 + 5*x2 >= 150, "protein_requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Servings of beans: {x1.x}")
    print(f"Servings of cereal: {x2.x}")
    print(f"Minimum cost: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```