To solve Bob's problem, we first need to translate the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of servings of chicken,
- $x_2$ as the number of servings of pork.

The objective function aims to minimize the total cost, which can be represented as $10x_1 + 15x_2$, since each serving of chicken costs $10 and each serving of pork costs $15.

The constraints are based on the required amounts of macro nutrients:
- Protein: $20x_1 + 15x_2 \geq 100$ (since each serving of chicken contains 20 units of protein and each serving of pork contains 15 units of protein, and at least 100 units are required),
- Carbs: $5x_1 + 3x_2 \geq 50$ (since each serving of chicken contains 5 units of carbs and each serving of pork contains 3 units of carbs, and at least 50 units are required),
- Fat: $6x_1 + 8x_2 \geq 30$ (since each serving of chicken contains 6 units of fat and each serving of pork contains 8 units of fat, and at least 30 units are required).

Also, since the number of servings cannot be negative, we have:
- $x_1 \geq 0$,
- $x_2 \geq 0$.

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'servings of chicken'), ('x2', 'servings of pork')],
    'objective_function': '10*x1 + 15*x2',
    'constraints': [
        '20*x1 + 15*x2 >= 100',
        '5*x1 + 3*x2 >= 50',
        '6*x1 + 8*x2 >= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

```python
from gurobipy import *

# Create a new model
m = Model("Bob's Diet")

# Create variables
x1 = m.addVar(name='servings_of_chicken', vtype=GRB.CONTINUOUS, lb=0)
x2 = m.addVar(name='servings_of_pork', vtype=GRB.CONTINUOUS, lb=0)

# Set the objective function
m.setObjective(10*x1 + 15*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(20*x1 + 15*x2 >= 100, name='protein_requirement')
m.addConstr(5*x1 + 3*x2 >= 50, name='carbs_requirement')
m.addConstr(6*x1 + 8*x2 >= 30, name='fat_requirement')

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Servings of chicken: {x1.x}")
    print(f"Servings of pork: {x2.x}")
    print(f"Total cost: ${10*x1.x + 15*x2.x:.2f}")
else:
    print("No optimal solution found")
```