## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the units of pork
- $x_2$ represents the units of shrimp

## Step 2: Formulate the objective function
The objective is to minimize the cost. The cost of pork is $3 per unit and the cost of shrimp is $5.5 per unit. Therefore, the objective function can be represented as:
\[ \text{Minimize:} \quad 3x_1 + 5.5x_2 \]

## 3: Define the constraints
The constraints are:
- The diet must include a minimum of 120 units of proteins. One unit of pork has 15 units of proteins and one unit of shrimp has 22 units of proteins.
- The diet must include a minimum of 30 units of fat. One unit of pork has 4 units of fat and one unit of shrimp has 7 units of fat.
- The units of pork and shrimp cannot be negative.

## 4: Write down the constraints in algebraic form
\[ 15x_1 + 22x_2 \geq 120 \]
\[ 4x_1 + 7x_2 \geq 30 \]
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

## 5: Symbolic representation of the problem
The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'pork'), ('x2', 'shrimp')],
    'objective_function': '3*x1 + 5.5*x2',
    'constraints': [
        '15*x1 + 22*x2 >= 120',
        '4*x1 + 7*x2 >= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 6: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code in Python:

```python
import gurobi

def solve_diet_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="pork", lb=0)
    x2 = model.addVar(name="shrimp", lb=0)

    # Objective function: minimize 3*x1 + 5.5*x2
    model.setObjective(3*x1 + 5.5*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(15*x1 + 22*x2 >= 120, name="protein_requirement")
    model.addConstr(4*x1 + 7*x2 >= 30, name="fat_requirement")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Pork: {x1.varValue}")
        print(f"Shrimp: {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_diet_problem()
```