To solve the given problem, we first need to define the variables and the objective function. Let's denote the number of servings of elk meat as \(E\) and the number of servings of bison meat as \(B\). The objective is to minimize the total cost, which can be represented as \(6E + 7B\), since each serving of elk meat costs $6 and each serving of bison meat costs $7.

The constraints are based on the nutritional requirements:
1. At least 30 units of iron must be consumed daily: \(5E + 4B \geq 30\)
2. At least 40 units of zinc must be consumed daily: \(3E + 4B \geq 40\)

Also, since we cannot have negative servings of meat, both \(E\) and \(B\) should be non-negative.

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
elk_servings = m.addVar(vtype=GRB.CONTINUOUS, name="Elk_Servings", lb=0)
bison_servings = m.addVar(vtype=GRB.CONTINUOUS, name="Bison_Servings", lb=0)

# Set the objective function: minimize cost
m.setObjective(6*elk_servings + 7*bison_servings, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*elk_servings + 4*bison_servings >= 30, name="Iron_Requirement")
m.addConstr(3*elk_servings + 4*bison_servings >= 40, name="Zinc_Requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Elk servings: {elk_servings.x}")
    print(f"Bison servings: {bison_servings.x}")
    print(f"Total cost: ${6*elk_servings.x + 7*bison_servings.x:.2f}")
else:
    print("No optimal solution found. The problem might be infeasible.")
```