To solve the problem described, let's first break down the natural language description into a symbolic representation. 

We have two food options: rice and beef. Let's denote:
- \(x_1\) as the number of servings of rice to consume per day,
- \(x_2\) as the number of servings of beef to consume per day.

The objective is to minimize the total cost, which can be represented by the objective function:
\[5x_1 + 30x_2\]

The constraints based on the macronutrient requirements are:
1. Protein: \(2x_1 + 20x_2 \geq 50\)
2. Carbs: \(80x_1 + 200x_2 \geq 1000\)
3. Fat: \(x_1 + 16x_2 \geq 40\)

Additionally, we have non-negativity constraints since the number of servings cannot be negative:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

Thus, the symbolic representation of the problem can be summarized as:

```json
{
    'sym_variables': [('x1', 'servings of rice'), ('x2', 'servings of beef')],
    'objective_function': '5*x1 + 30*x2',
    'constraints': ['2*x1 + 20*x2 >= 50', '80*x1 + 200*x2 >= 1000', 'x1 + 16*x2 >= 40', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_rice")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="servings_of_beef")

# Set the objective function
m.setObjective(5*x1 + 30*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 20*x2 >= 50, "protein_constraint")
m.addConstr(80*x1 + 200*x2 >= 1000, "carbs_constraint")
m.addConstr(x1 + 16*x2 >= 40, "fat_constraint")

# Optimize the model
m.optimize()

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