To solve Bob's problem, we need to set up a linear programming model that minimizes the total cost of food while meeting the daily requirements of protein, carbs, and fat. Let's denote:

- $x_c$ as the number of servings of chicken,
- $x_p$ as the number of servings of pork.

The objective function is to minimize the total cost, which can be represented as $10x_c + 15x_p$, since a serving of chicken costs $10 and a serving of pork costs $15.

We have constraints based on the nutritional requirements:
1. For protein: $20x_c + 15x_p \geq 100$ (since each serving of chicken contains 20 units of protein and each serving of pork contains 15 units of protein, and Bob needs at least 100 units of protein per day).
2. For carbs: $5x_c + 3x_p \geq 50$ (since each serving of chicken contains 5 units of carbs and each serving of pork contains 3 units of carbs, and Bob needs at least 50 units of carbs per day).
3. For fat: $6x_c + 8x_p \geq 30$ (since each serving of chicken contains 6 units of fat and each serving of pork contains 8 units of fat, and Bob needs at least 30 units of fat per day).

Also, since we cannot have negative servings of food, $x_c \geq 0$ and $x_p \geq 0$.

Now, let's write the Gurobi code to solve this problem:

```python
from gurobipy import *

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

# Define variables
chicken_servings = m.addVar(lb=0, name="chicken_servings")
pork_servings = m.addVar(lb=0, name="pork_servings")

# Objective function: minimize cost
m.setObjective(10 * chicken_servings + 15 * pork_servings, GRB.MINIMIZE)

# Constraints
m.addConstr(20 * chicken_servings + 15 * pork_servings >= 100, "Protein_Requirement")
m.addConstr(5 * chicken_servings + 3 * pork_servings >= 50, "Carb_Requirement")
m.addConstr(6 * chicken_servings + 8 * pork_servings >= 30, "Fat_Requirement")

# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Chicken servings: {chicken_servings.x}")
    print(f"Pork servings: {pork_servings.x}")
    print(f"Total cost: ${10 * chicken_servings.x + 15 * pork_servings.x:.2f}")
else:
    print("No optimal solution found. The problem might be infeasible.")

```
```python
```