To solve this optimization problem, we first need to define our decision variables and the constraints based on the given conditions. Let's denote:

- \(x_v\) as the amount invested in the vaccine industry.
- \(x_m\) as the amount invested in the meat-replacement industry.

The total budget is $100,000, so we have:
\[x_v + x_m \leq 100,000\]

We want at least 60% of our investment to be in the meat-replacement industry, which gives us:
\[x_m \geq 0.6 \times 100,000\]
\[x_m \geq 60,000\]

Additionally, we can invest at most 30% of our total budget in the vaccine industry, leading to:
\[x_v \leq 0.3 \times 100,000\]
\[x_v \leq 30,000\]

The return from each investment is as follows:
- The vaccine industry yields a 5% return, so the profit from \(x_v\) dollars invested is \(0.05x_v\).
- The meat-replacement industry yields a 7% return, so the profit from \(x_m\) dollars invested is \(0.07x_m\).

Our objective is to maximize the total return:
\[Maximize: 0.05x_v + 0.07x_m\]

Given these constraints and objectives, we can formulate this problem in Gurobi as follows:

```python
from gurobipy import *

# Create a new model
m = Model("Investment_Optimization")

# Define the decision variables
x_v = m.addVar(name="vaccine_investment", lb=0)
x_m = m.addVar(name="meat_replacement_investment", lb=0)

# Add constraints
m.addConstr(x_v + x_m <= 100000, name="total_budget")
m.addConstr(x_m >= 60000, name="min_meat_replacement")
m.addConstr(x_v <= 30000, name="max_vaccine")

# Set the objective function
m.setObjective(0.05*x_v + 0.07*x_m, GRB.MAXIMIZE)

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
print(f"Objective: {m.objVal}")
```