## Step 1: Define the symbolic representation of the problem

Let's denote the servings of elk meat as \(x_1\) and the servings of bison meat as \(x_2\). The objective is to minimize the cost, which can be represented as \(6x_1 + 7x_2\). The constraints are that at least 30 units of iron and 40 units of zinc must be consumed daily. Given that a serving of elk meat contains 5 units of iron and 3 units of zinc, and a serving of bison meat contains 4 units of iron and 4 units of zinc, the constraints can be written as:
- \(5x_1 + 4x_2 \geq 30\) for iron
- \(3x_1 + 4x_2 \geq 40\) for zinc
Also, \(x_1 \geq 0\) and \(x_2 \geq 0\) because the servings cannot be negative.

## Step 2: Convert the problem into a symbolic representation

The symbolic variables are:
- \(x_1\) for servings of elk meat
- \(x_2\) for servings of bison meat

The objective function to minimize is:
- \(6x_1 + 7x_2\)

The constraints are:
- \(5x_1 + 4x_2 \geq 30\)
- \(3x_1 + 4x_2 \geq 40\)
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

## 3: Represent the problem in the requested JSON format

```json
{
'sym_variables': [('x1', 'elk meat servings'), ('x2', 'bison meat servings')],
'objective_function': '6*x1 + 7*x2',
'constraints': [
    '5*x1 + 4*x2 >= 30',
    '3*x1 + 4*x2 >= 40',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Implement the problem in Gurobi Python

```python
import gurobi

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

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

    # Define the objective function
    model.setObjective(6 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(5 * x1 + 4 * x2 >= 30, name="iron_constraint")
    model.addConstr(3 * x1 + 4 * x2 >= 40, name="zinc_constraint")

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Elk meat servings: {x1.varValue}")
        print(f"Bison meat servings: {x2.varValue}")
        print(f"Total cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```